Medical Image Segmentation

The presented method addresses the problem of multi-spectral image segmentation. Multiple images of different modalities are used to improve segmentation, as better tissue separation can be achieved in a higher dimensional space. We use a model which takes into account the physical process of the medical image formation. In particular the method addresses the problem of partial volumes of tissues that are present in a single voxel at tissue boundaries. The parameters of the multi-dimensional tissue model are iteratively adjusted using an Expectation Maximisation (EM) optimisation technique. Bayes theory is used to generate probability maps for each segmented tissue which estimates the most likely tissue volume fraction within each voxel as opposed to previous approaches which attempt to compute how likely a certain grey level would be generated by a particular tissue class.

Skin diseases are among the most common health problems worldwide. In this article we proposed a method that uses computer vision based techniques to detect various kinds of dermatological skin diseases. We have used different types of image processing algorithms for feature extraction and feed forward artificial neural network for training and testing purpose. The system works on two phases- first pre-process the colour skin images to extract significant features and later identifies the diseases. The system successfully detects 9 different types of dermatological skin diseases with an accuracy rate of 90%.

This report describes the design, implementation, evaluation and original enhancements to the Live-Wire method for 2D and 3D image segmentation. Live-Wire 2D employs a semi-automatic paradigm; the user is asked to select a few boundary points of the object to segment, to steer the process in the right direction, while the result is displayed in real time. In our implementation segmentation is extended to three dimensions by performing this process on a slice-by-slice basis. User's time and involvement is further reduced by allowing him to specify object contours in planes orthogonal to the slices. If these planes are chosen strategically, Live-Wire 3D can perform 2D segmentation in the plane of each slice automatically. This report also proposes two improvements to the original method, path heating and a new graph edge feature function based on variance of path properties along the boundary. We show that these improvements lead up to a 33% reduction in interaction with the user, and improved delineation in presence of strong interfering edges.

Image segmentation is an essential step in many advanced imaging applications. Accurate segmentation is required for volume measurements, medical diagnosis, image guided procedures, and 3D rendering. In this paper, we have developed a new multiresolution algorithm that extends the well-known expectation maximization (EM) algorithm. The proposed algorithm is based on generating scaled versions of the original image using wavelet analysis. We used synthetic images for tests and produced the results in ...

—Hough transform (HT) is a typical method to detect or segment geometry objects from images. In this paper, we study the principle of Hough Transform and its mathematical expressions, and try to use a new approach based on Hough transform for quick line and circle detection in image processing. Our method accurately detected some simple graphics, such as straight line of different direction, circles of different detection, thickness and different number. The results show that our method is less memory consumption and calculated fast, which could be applied for line detection and segmentation in 3D ultrasonic image.

Watershed transformation is a common technique for image segmentation. However, its use for automatic medical image segmentation has been limited particularly due to oversegmentation and sensitivity to noise. Employing prior shape knowledge has demonstrated robust improvements to medical image segmentation algorithms. We propose a novel method for enhancing watershed segmentation by utilizing prior shape and appearance knowledge. Our method iteratively aligns a shape histogram with the result of an improved k-means clustering algorithm of the watershed segments. Quantitative validation of magnetic resonance imaging segmentation results supports the robust nature of our method.

This paper makes a review on the current segmentation algorithms used for medical images. Algorithms are divided into three categories according to their main ideas: the ones based on threshold, the ones based on pattern recognition techniques and the ones based on deformable models. The main tendency of each category with their principle ideas, application field, advantages and disadvantages are discussed. For each considered type some typical algorithms are described. Algorithms of the third category are mainly focused because of the intensive investigation on deformable models in the recent years. Possible applications of these algorithms on segmenting organs and tissues contained in the pelvic cavity are also discussed through several preliminary experiments.

Skin diseases are among the most common health
problems worldwide. In this article we proposed a method that
uses computer vision based techniques to detect various kinds of dermatological skin diseases. We have used different types of image processing algorithms for feature extraction and feed
forward artificial neural network for training and testing purpose. The system works on two phases- first pre-process the
colour skin images to extract significant features and later
identifies the diseases. The system successfully detects 9 different types of dermatological skin diseases with an accuracy rate of 90%.

Image segmentation is still a debatable problem although there have been many research work done in the last few decades. First of all, every solution for image segmentation is problem-based. Secondly, medical image segmentation methods generally have restrictions because medical images have very similar gray level and texture among the interested objects. Therefore, this dissertation presents a framework to extract simultaneity several objects of interest from head Computed Tomography images.

Image segmentation is a key technique in image processing with the goal to extract important objects from the image. This evaluation study focuses on the segmentation quality of three different interactive segmentation techniques, namely Region Growing (RG), Watershed (WS) and the cellular automaton based GrowCut (GC) algorithm. Three different evaluation measures are computed to compare the segmentation quality of each algorithm: Rand Index (RI), Mutual Information (MI), and the Dice Coefficient (D). For the images in the publicly available ground truth data base utilized for the evaluation, the GrowCut method has a slight advantage over the other two. The presented results provide insight into the performance and the characteristics with respect to the image quality of each tested algorithm.

Przedstawiony tekst zawiera streszczenie referatu, wygłoszonego na zaproszenie organizatorów konferencji „Narzędzia teleinformatyczne w diagnostyce. Rozwój środowiska e-Health z wykorzystaniem technologii cyfrowych”. Konferencję zorganizowali łącznie Centrum Promocji Informatyki oraz Centrum Edukacji Medycznej. W materiałach konferencji (drukowanych oraz na CD) po przedstawionym tu streszczeniu zamieszczona jest pełna prezentacja PPT, zajmująca stronice aż do 273 włącznie.

Image segmentation still requires improvements although there have been research work since the last few decades. This is due to some factors. Firstly, most image segmentation solution is problem-based. Secondly, medical image segmentation methods generally have restrictions because medical images have very similar gray level and texture among the interested objects. The goal of this work is to design a framework to extract simultaneously several objects of interest from Computed Tomography (CT) images. Our method does not need a large training set or priori knowledge. The learning phase is based on reinforcement learning (RL). The input image is divided into several sub-images, and each RL agent works on it to find the suitable value for each object in the image. Each state in the environment has associated defined actions, and a reward function computes reward for each action of the RL agent. Finally the valuable information is stored in a Q-Matrix, and the final result can be applied in segmentation of new similar images. The experimental results for cranial CT images demonstrated segmentation accuracy above 93%.

This paper presents a review of the current liter- ature on rough set and near set-based approaches to solving various problems in medical imaging such as medical image segmentation, object extraction and image classification. Rough set frameworks hybridized with other computational intelligence technologies that include neural networks, particle swarm op- timization, support vector machines and fuzzy sets are also presented.

The ability of Minkowski Functionals to characterize local structure in different biological tissue types has been demonstrated in a variety of medical image processing tasks. We introduce anisotropic Minkowski Functionals (AMFs) as a novel variant that captures the inherent anisotropy of the underlying gray-level structures. To quantify the anisotropy characterized by our approach, we further introduce a method to compute a quantitative measure motivated by a technique utilized in MR diffusion tensor imaging, namely fractional anisotropy. We showcase the applicability of our method in the research context of characterizing the local structure properties of trabecular bone micro-architecture in the proximal femur as visualized on multi-detector CT. To this end, AMFs were computed locally for each pixel of ROIs extracted from the head, neck and trochanter regions. Fractional anisotropy was then used to quantify the local anisotropy of the trabecular structures found in these ROIs and to compare its distribution in different anatomical regions. Our results suggest a significantly greater concentration of anisotropic trabecular structures in the head and neck regions when compared to the trochanter region (p < 10-4). We also evaluated the ability of such AMFs to predict bone strength in the femoral head of proximal femur specimens obtained from 50 donors. Our results suggest that such AMFs, when used in conjunction with multi-regression models, can outperform more conventional features such as BMD in predicting failure load. We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding directional attributes of local structure, which may be useful in a wide scope of biomedical imaging applications.

Cancer has become a leading cause of death worldwide. To deal with medical images to discover tumors and their types, Authors need a distinct experience in understanding medical images. Authors need machine learning techniques to reach great accuracy and speed to analyse these images to avoid a lack of experience or errors. In this paper, Authors will study a (SVM) of machine learning techniques used to classify brain images. SVM will be used in this paper to analyse brain images and discover Benign Tumor and Malignant tumor by using Matlab software. The results of the experiments conducted showed the accuracy of the system provided for the classification of tumor types (Benign, Malignant) found in medical brain images. Authors will adhere in this research that the images to be classified are limited by the presence of only two types of tumors. In the future, some pre-processing procedures will be added to the brain's medical images prior to the classification process.

The paper focuses on the automatic cardiac diagnostic challenge (ACDC) and more specifically on LV segmentation in MRI images. Two fully automatic segmentation methods, i.e., k-means and deep learning, will be presented, and discussed in detail. The implementation of each method will be described and both methods will be unified in a GUI tool for better usability. The tool is a user interface application which developed in Python and wrapped under MATLAB to give better access for further development. The objective of the tool is to segment the LV during diastole and systole (only the endocardium) in MRI images and calculate ventricle cavity volume and ejection fraction. The methods were tested on MRI images of a database made of 100 patients. The experimental evaluation demonstrates promising results and significant precision for deep learning algorithm on entire database while k-means might prone to failure if intensity changes significantly. The deep learning algorithm using TensorFlow backbone was trained several times with different training dataset and the results were compared. The findal findings shows that if the number of the images used in training increases and the diversity of the selected images covers broad specturm of the scenarios then the precsion in practice increases significantly comparing other LV segementation methods. The result shows 50% accuracy improvement when database size increases four times.

This paper introduces a new method to medical image segmentation using a reinforcement learning scheme. We use this novel idea as an effective way to optimally find the appropriate local thresholding and structuring element values and segment the prostate in ultrasound images. Reinforcement learning agent uses an ultrasound image and its manually segmented version and takes some actions (i.e., different thresholding and structuring element values) to change the environment (the quality of segmented image). The agent is provided with a scalar reinforcement signal determined objectively. The agent uses these objective reward/punishment to explore/exploit the solution space. The values obtained using this way can be used as valuable knowledge to fill a Q-matrix. The reinforcement learning agent can use this knowledge for similar ultrasound images as well. The results demonstrate high potential for applying reinforcement learning in the field of medical image segmentation.

Abstract-The corpus callosum is one of the most important structures in human brain. Most of the neurological disorders reflect directly or indirectly on the morphological features of Corpus Callosum. The mid-sagittal brain Magnetic Resonance images fully describe the anatomical structure of corpus callosum. Often considered challenging task of segmenting Corpus Callosum from Magnetic Resonance images has proved the importance of studies on Corpus Callosum segmentation. In this paper, a K-means clustering algorithm is proposed for segmentation of the region of Corpus Callosum. The results of segmentation can be used further for feature extraction and classification for medical diagnosis.

Keywords-Corpus Callosum, K-means clustering, Segmentation and Magnetic Resonance Image

Scope of the book:
This book focusses on the technical concepts of deep learning and its associated branch Neural Networks for the various dimensions of image processing applications. The proposed volume intends to bring together researchers to report the latest results or progress in the development of the above-mentioned areas. Since there is a deficit of books on this specific subject matter, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings.
Topics of Interest:
This book solicits contributions, which include the fundamentals in the field of Deep Artificial Neural Networks and Image Processing supported by case studies and practical examples. Each chapter is expected to be self-contained and to cover an in-depth analysis of real life applications of neural networks to image analysis.

For medical applications used in diagnostics, planning and guidance, segmentation of anatomical structures from modalities like computed tomography and ultrasound is a key enabling technology. Most segmentation methods are computationally expensive and as more and more data is becoming available for any given patient, faster implementations are needed. The increased programmability of Graphic Processing Units (GPUs) in recent years have enabled their use in several areas. The GPUs can solve large data parallel problems at much greater speed than the traditional CPU while being more affordable and energy efficient than distributed systems. Furthermore, the GPUs allow for concurrent visualization and interactive segmentation, where the user can help the algorithm to achieve a satisfactory result. In this review, the use of GPUs to accelerate medical image segmentation methods is investigated. A set of criteria for efficient use of GPUs are defined and each segmentation method is rated accordingly. In addition, references to relevant GPU implementations and insight on GPU optimization are provided and discussed. The conclusion of the review is that almost all segmentation methods can benefit from GPU processing due to the methods’ data parallel structure and high thread count. However, factors such as synchronization, branch divergence and memory usage can limit the speedup.

Accurate segmentation of 2-D, 3-D, and 4-D medical images to isolate 5 anatomical objects of interest for analysis is essential in almost any computer-aided 6 diagnosis system or other medical imaging applications. Various aspects of segmen-7 tation features and algorithms have been extensively explored for many years in a 8 host of publications. However, the problem remains challenging, with no general and 9 unique solution, due to a large and constantly growing number of different objects of 10 interest, large variations of their properties in images, different medical imaging 11 modalities, and associated changes of signal homogeneity, variability, and noise for 12 each object. This chapter overviews most popular medical image segmentation 13 techniques and discusses their capabilities, and basic advantages and limitations. 14 The state-of-the-art techniques of the last decade are also outlined.

A study of the calculation of pleural effusion index (PEI) in patient with dengue hemorrhagic fever (DHF) has been conducted. PEI calculation was done through matlab programming language. Some digital image processing methods used in this research were thresholding segmentation, morphology operation, and calculation of pixel number per column in image to get PEI value. PEI values generated from image processing can be an alternative to replace the manual calculations done by physicians and used to demonstrate the gravity level of DHF.

In this thesis, we address two problems in medical image processing: (1) Lossless compression of 3D MR images (2) Modeling of fMRI time series for the estimation of haemodynamic response function (HRF).

For the lossless compression of 3D MR images, we propose two schemes. In the first scheme, a nonuniformly sized mesh is superimposed on the image, designed based on the content of the image. Affine transformations are used to estimate the mesh elements of a MR slice using the previous slice. The residue between the actual slice and the estimated slice is entropy coded. Context based entropy coding is used to enhance the performance of the entropy coding. The second is a wavelet based scheme wherein important functionalities like region of interest coding, progressive transmission and lossless compression capability are provided. 2D and 3D separable integer wavelet transforms are used for lossless compression. A context based entropy coding scheme is also used here to code the resulting bit stream. These two schemes are tested on two datasets and compared with other state-of-the-art schemes available in the literature. The bit rate obtained with our schemes of about 2 bits per voxel is comparable to the other schemes.

For estimating the HRF from fMRI data, we proposed two probability models. In the first model, the fMRI time series is assumed to be a stationary model. Probabilistic principal component analysis is used to model the time series. Using this model, one can represent trends and other confounding signals in the fMRI time series in terms of its dominant eigen vectors. The HRF function is modeled using both parametric and nonparametric models. In the second model, the time series is assumed to be non-stationary and is modeled as a fractional Brownian motion. We use the property that wavelet transform with sufficient number of vanishing moments decorrelates fBm processes. The methods are tested both on synthetic data at various SNRs and on a real null fMRI data. We compare these methods with an existing method, where the trends in the fMRI time series are modeled by polynomial bases. The proposed methods performed better with respect to the accuracy of HRF estimation. We also developed an algorithm which classifies fMRI voxels according to their HRF characteristics. This algorithm will be useful in the event related design paradigms. In this scheme, we model the class labels as a Markov random field and the class labels and HRFs are estimated iteratively using an expectation maximization algorithm. This algorithm is tested on a real null fMRI data.

Statistical shape models (SSMs) have by now been firmly established as a robust tool for segmentation of medical images. While 2D models have been in use since the early 1990s, widespread utilization of three-dimensional models appeared only in recent years, primarily made possible by breakthroughs in automatic detection of shape correspondences. In this article, we review the techniques required to create and employ these 3D SSMs. While we concentrate on landmark-based shape representations and thoroughly examine the most popular variants of Active Shape and Active Appearance models, we also describe several alternative approaches to statistical shape modeling. Structured into the topics of shape representation, model construction, shape correspondence, local appearance models and search algorithms, we present an overview of the current state of the art in the field. We conclude with a survey of applications in the medical field and a discussion of future developments.

Worldwide incidence rate of prostate cancer has progressively increased with time especially with the increased proportion of elderly population. Early detection of prostate cancer when it is confined to the prostate gland has the best chance of successful treatment and increase in surviving rate. Prostate cancer occurrence rate varies over the three prostate regions, peripheral zone (PZ), transitional zone (TZ), and central zone (CZ) and this characteristic is one of the important considerations is development of segmentation algorithm. In fact, the occurrence rate of cancer PZ, TZ and CZ regions is respectively. at 70-80%, 10-20%, 5% or less. In general application of medical imaging, segmentation tasks can be time consuming for the expert to delineate the region of interest, especially when involving large numbers of images. In addition, the manual segmentation is subjective depending on the expert's experience. Hence, the need to develop automatic segmentation algorithms has rapidly increased along with the increased need of diagnostic tools for assisting medical practitioners, especially in the absence of radiologists. The prostate gland segmentation is challenging due to its shape variability in each zone from patient to patient and different tumor levels in each zone. This survey reviewed 22 machine learning and 88 deep learning-based segmentation of prostate MRI papers, including all MRI modalities. The review coverage includes the initial screening and imaging techniques, image pre-processing, segmentation techniques based on machine learning and deep learning techniques. Particular attention is given to different loss functions used for training segmentation based on deep learning techniques. Besides, a summary of publicly available prostate MRI image datasets is also provided. Finally, the future challenges and limitations of current deep learning-based approaches and suggestions of potential future research are also discussed. INDEX TERMS MRI, prostate cancer, deep learning, automatic algorithms, prostate gland.

Many image segmentation solutions are problem-based. Medical images have very similar grey level and texture among the interested objects. Therefore, medical image segmentation requires improvements although there have been researches done since the last few decades. We design a self-learning framework to extract several objects of interest simultaneously from Computed Tomography (CT) images. Our segmentation method has a learning phase that is based on reinforcement learning (RL) system. Each RL agent works on a particular sub-image of an input image to find a suitable value for each object in it. The RL system is define by state, action and reward. We defined some actions for each state in the sub-image. A reward function computes reward for each action of the RL agent. Finally, the valuable information, from discovering all states of the interest objects, will be stored in a Q-matrix and the final result can be applied in segmentation of similar images. The experimental results for cranial CT images demonstrated segmentation accuracy above 95%.

Breast Cancer is the most common Cancer among women. Currently there are no methods to prevent breast cancer that is the reason why early detection represents a very important factor in cancer treatment. Mammography is currently the best available radiological technique for early detection of breast cancer. Mass detection using Computer Aided Design and Detection systems serve as a second decision tool to radiologists for discovering masses in the mammograms. This work presents a methodology for segmentation of masses in Mammogram Images. The proposed algorithm uses Median filtering, Optimal Global thresholding using Otsu's method and morphological processing in order to: (1) enhance quality of mammography image, (2) Segmentation of masses based on threshold resulting Region of Interest (ROI)(3) extracting segmented masses from image. The proposed techniques are implemented and tested under MATLAB environment.

Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

Cancer is one of the leading causes of death worldwide. Radiotherapy is a standard treatment for this condition and the first step of the radiotherapy process is to identify the target volumes to be targeted and the healthy organs at risk (OAR) to be protected. Unlike previous methods for automatic segmentation of OAR that typically use local information and individually segment each OAR, in this paper, we propose a deep learning framework for the joint segmentation of OAR in CT images of the thorax, specifically the heart, esophagus, trachea and the aorta. Making use of Fully Convolutional Networks (FCN), we present several extensions that improve the performance, including a new architecture that allows to use low level features with high level information, effectively combining local and global information for improving the localization accuracy. Finally, by using Conditional Random Fields (specifically the CRF as Recurrent Neural Network model), we are able to account for relationships between the organs to further improve the segmentation results. Experiments demonstrate competitive performance on a dataset of 30 CT scans.

In computer vision, image segmentation is always
selected as a major research topic by researchers. Due to its vital
rule in image processing, there always arises the need of a better
image segmentation method. Clustering is an unsupervised study
with its application in almost every field of science and
engineering. Many researchers used clustering in image
segmentation process. But still there requires improvement of
such approaches. In this paper, a novel approach for clustering
based image segmentation is proposed. Here, we give importance
on color space and choose l*a*b* for this task. The famous hard
clustering algorithm K-means is used, but as its performance is
dependent on choosing a proper distance measure, so, we go for
“cosine” distance measure. Then the segmented image is filtered
with sobel filter. The filtered image is analyzed with marker
watershed algorithm to have the final segmented result of our
original image. The MSE and PSNR values are evaluated to
observe the performance.

The watershed transform has interesting properties that make it useful for many different image segmentation applications: it is simple and intuitive, can be parallelized, and always produces a complete division of the image. However, when applied to medical image analysis, it has important drawbacks (oversegmentation, sensitivity to noise, poor detection of thin or low signal to noise ratio structures). We present an improvement to the watershed transform that enables the introduction of prior information in its calculation. We propose to introduce this information via the use of a previous probability calculation. Furthermore, we introduce a method to combine the watershed transform and atlas registration, through the use of markers. We have applied our new algorithm to two challenging applications: knee cartilage and gray matter/white matter segmentation in MR images. Numerical validation of the results is provided, demonstrating the strength of the algorithm for medical image segmentation.

In this paper, we provide a comparative evaluation of preprocessing methods for microaneurysm detection in color fundus images. Our aim is to achieve high sensitivity values in the candidate extraction phase. This requirement can be fulfilled if we select an appropriate preprocessing method for the candidate extractor. Our experiments showed that Contrast Limited Adaptive Histogram Equalization achieved the best performance among the investigated preprocessing methods. Moreover, we show that certain relationship also exists between the specificity and the information content of the images. This observation can help us to select preprocessing methods for which we do not have prior information regarding microaneurysm detection.

Deep learning models perform best when tested on target (test) data domains whose distribution is similar to the set of source (train) domains. However, model generalization can be hindered when there is significant difference in the underlying statistics between the target and source domains. In this work, we adapt a domain generalization method based on a model-agnostic meta-learning framework [1] to biomedical imaging. The method learns a domain-agnostic feature representation to improve generalization of models to the unseen test distribution. The method can be used for any imaging task, as it does not depend on the underlying model architecture. We validate the approach through a computed tomography (CT) vertebrae segmentation task across healthy and pathological cases on three datasets. Next, we employ few-shot learning, i.e. training the generalized model using very few examples from the unseen domain, to quickly adapt the model to new unseen data distribution. Our results suggest that the method could help generalize models across different medical centers, image acquisition protocols , anatomies, different regions in a given scan, healthy and diseased populations across varied imaging modalities.

The detection of double edges in x-ray images of lumbar vertebrae is of prime importance in the assessment of vertebral injury or collapse that may be caused by osteoporosis and other spine pathology. In addition, if the above double-edge detection process is conducted within an automatic framework, it would not only facilitate inexpensive and fast means of obtaining objective morphometric measurements on the spine, but also remove the human subjectivity involved in the morphometric analysis. This paper proposes a novel force-formulation scheme, termed as Pressurized Open Directional Gradient Vector Flow snakes, to discriminate and detect the superior and inferior double edges present in the radiographic images of the lumbar vertebrae. As part of the validation process, this algorithm is applied to a set of 100 lumbar images and the detection results are quantified using analyst-generated ground truth. The promising nature of the detection results bears testimony to the efficacy of the proposed approach

Now-a-days Cluster computing has become a crying need for the processing of large scale data. For computing large amount of data, which need huge execution time, the run time can be reduced using multiple processors and task distribution through cluster computing. It is the technique of sharing two or more computers' resources through a network (usually through a local area network) in order to take advantage of the parallel processing power of those computers. Clusters of computers are usually deployed to improve processing speed and/or reliability and scalability over that provided by a single computer. In this paper we proposed a High Performance computing approach on Linux platform (Ubuntu) using Parallel Programming environment with the collaboration of multiple nodes for large scale computational work.

The problem of texture Segmentation involves subdividing an image into differently textured regions. Gabor filters produce outputs wh ich are notably distinct for the different textured regions. Detecting the discontinuity in the filters output and their statistical properties help in segmenting and classifying a given image with different texture regions. Feature calculation for every pixel in the image reduces the computational cost for color based segmentation. In this proposed method, firstly image texture segmentation is performed by using Gabor filter and the result obtained from Gabor filter differentiates the textures with a variety of colors as different textures, and these similar colors are extracted as different images by color-based segmentation with K means clustering and finally the features are extracted by using first and second order statistical methods. The features extracted from first and second order statistical methods are given to PNN

Medical image segmentation plays an important role in treatment planning, identifying tumors, tumor volume, patient follow up and computer guided surgery. There are various techniques for medical image segmentation. This paper presents a image segmentation technique for locating brain tumor(Astrocytoma-A type of brain tumor).Proposed work has been divided in two phases-In the first phase MRI image database(Astrocytoma grade I to IV) is collected and then preprocessing is done to improve quality of image. Second-phase includes three steps-Feature extraction, Feature selection and Image segmentation. For feature extraction proposed work uses GLCM (Grey Level co-occurrence matrix).To improve accuracy only a subset of feature is selected using hybrid Genetic algorithm(Genetic Algorithm+fuzzy rough set) and based on these features fuzzy rules and membership functions are defined for segmenting brain tumor from MRI images of .ANFIS is a adaptive network which combines benefits of both fuzzy and neural network .Finally, a comparative analysis is performed between ANFIS, neural network, Fuzzy ,FCM,K-NN, DWT+SOM,DWT+PCA+KN, Texture combined +ANN, Texture Combined+ SVM in terms of sensitivity ,specificity ,accuracy.

Authors:- V. Antony Asir Daniel1, J. Surendiran2, K. Kalaiselvi3
Abstract- Red blood cells are specialized as oxygen carrier RBC plays a crucial role in
medical diagnosis and pathological study. The blood samples are collected using the smear
glass slide. These samples are taken under the test using the image of the blood. Filtering
process are carries out to remove the noise. Morphological operation are applied on the
blood image and using Hough transform method the RBC are counted which is the
effective segmentation process.

Segmentation is required in Echocardiographic Images for analyzing images for finding function of the heart or finding out related disease. In this paper we implement different thresholding methods on Echocardiographic Images. We also compare different Thresholding Techniques. For studying areas and volume of ventricle and atrium we require thresholding of the image. Thresholding also can be used as pre-processing in some application in Echocardiographic Images. Here we implement and compare three methods of thresholding.

Eye disease has become serious concern to people, specially who have been suffering from diabetes. Systematic eye diseases are manifest in around the retina of an eye. Digital photography of the retinal images provide a significant information about the disease. This kind of retinal images work as a powerful source for the doctors and ophthalmologist to take meaningful decision about the disease. This kind of Computer Aided Diagnosis(CAD) allow both the doctors and patient to take a second layer of satisfaction. Considering this issues, this paper segmented the retinal vessel images using the state-of-the-art Deep Neural Network(DNN) such as a combination of U-Net and Generative Adversarial Network (GAN) model. As the retinal images suffers due to the Contrast and Illumination problems, this paper also proposed two preprocessing algorithms to reduce this effect.

For some applications, such as image recognition or compression, we cannot process the whole image directly for the reason that it is inefficient and unpractical. Therefore, several image segmentation algorithms were proposed to segment an image before recognition or compression. Image segmentation is to classify or cluster an image into several parts (regions) according to the feature of image, for example, the pixel value or the frequency response. Up to now, lots of image segmentation algorithms exist and be extensively applied in science and daily life. According to their segmentation method, we can approximately categorize them into region-based seg-mentation, data clustering, and edge-base segmentation. In this tutorial, we survey several popular image segmentation algorithms, discuss their specialties, and show their segmentation results. Moreover, some segmentation applications are described in the end.