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This paper presents an algorithm to classify pixels in uterine cervix images into two classes, namely normal and abnormal tissues, and simultaneously select relevant features, using group sparsity. Because of the large variations in image appearance due to changes of illumination, specular reflections and other visual noise, the two classes have a strong overlap in feature space, whether features...
In this paper, we propose a new segmentation algorithm that combines a graph-based shape model with image cues based on boosted features. The landmark-based shape model encodes prior constraints through the normalized Euclidean distances between pairs of control points, alleviating the need of a large database for the training. Moreover, the graph topology is deduced from the dataset using manifold...
We describe a technique for segmenting individual prostatic glands in hematoxylin-and-eosin stained prostatic tissue images. The method begins with image artifact correction, then segments the image into four tissue components using principal component analysis and k-means clustering, and finally identifies glands using a region-growing algorithm. We calculated the average gland size to distinguish...
In this paper, the problem of medical image segmentation is addressed in an unsupervised framework. We propose a novel method considering the hidden Markov random field model (HMRF) to model the image class labels, which takes into account the mutual influences of neighboring sites formulated on the basis of fuzzy clustering principle. The model parameters, number of class labels and the image labels...
A new technique for automatic extraction of object region and boundary from the background for cell nucleus segmentation of cervical cancer images is proposed in this work. Gradient magnitude and directional information are employed to extract the exact boundary of the object under consideration. Segmentation process begins with preprocess as computation of optimum threshold based on the clusters...
Implementation of a neuro-fuzzy segmentation process of the MRI data is presented in this study to detect various tissues like white matter, gray matter, csf and tumor. The advantage of hierarchical self organizing map and fuzzy c means algorithms are used to classify the image layer by layer. The lowest level weight vector is achieved by the abstraction level. We have also achieved a higher value...
Image thresholding is an important technique for image processing and pattern recognition. Several thresholding techniques have been proposed in the literature. In this paper for segmentation of magnetic resonance images, a novel method using a combination of the multilevel thresholding algorithm and the hierarchical evolutionary algorithm (HEA) is proposed. The HEA can be viewed as a variant of conventional...
In this paper, an automatic k-mean clustering based on C-Y colour model was proposed. First, initial filter is used to remove the tissues images which remain blue after counterstaining process. Secondly, k-mean clustering using saturation component of C-Y colour model is used to segment the TB bacilli from its undesirable background which also remains red even after decolourization process. Thirdly,...
This paper describes a new method developed for fusion of X-ray and fluorescent molecular tomography (FMT) images. For easier diagnostics, images obtained from X-ray and FMT sources are fused to generate perceptibly informative image display using the spatial and spectral domain properties of the images. The basic premise in this research originates from the fact that in medical imaging not all the...
A key parameter in metabolic and pathologic studies is the estimation of body tissue distribution. This is a laborious and operator-dependent process. In this work we introduce an unsupervised muscle and fat quantification algorithm based on water only, fat only and water-and-fat MRI images of the mid-thigh area. We first use parametric deformable models to segment the subcutaneous fat and then apply...
This paper presented an approach for automatically quantifying the dental plaque based on modified kernelized fuzzy c-means. The proposed approach was applied to a clinical database consisting of 30 objects. The experimental results show that the proposed method provids accurate quantitative measurement of dental plaque compared with that of traditional manual measurement indices of the dental plaque.
This paper proposes Improved Mountain Clustering version-2 (IMC-2) based medical image segmentation. The proposed technique is a more powerful approach for medical image based diagnosing diseases like brain tumor, tooth decay, lung cancer, tuberculosis etc. The IMC-2 based medical image segmentation approach has been applied on various categories of images including MRI images, dental X-rays, chest...
Automated methods for image segmentation, image registration, clustering of images and probabilistic atlas construction are of great interest in medical image analysis. In this work, we propose a model where these different aspects are combined in one comprehensive probabilistic framework. The framework is formulated as an EM optimization algorithm. Validation is performed on simulated and real images...
Accurate analysis of 2D echocardiographic images is vital for diagnosis and treatment of heart related diseases. For this task, extraction of cardiac borders must be carried out. In particular, automatic quantitative measurements of Left Ventricle (LV), Right Ventricle (RV), Left Atrium (LA), Right Atrium, Valve size, etc. are essential. We believe that automatic processing of these echo images could...
In this paper, we present a graph-based multi-resolution approach for mitosis extraction in breast cancer histological whole slide images. The proposed segmentation uses a multi-resolution approach which reproduces the slide examination done by a pathologist. Each resolution level is analyzed with a focus of attention resulting from a coarser resolution level analysis. At each resolution level, a...
An efficient K-Means clustering algorithm is proposed using the power of SQL in a relational Database Management environment. Further, this method is applied to segment 2D echocardiography images. We propose this method mainly to improve the speed of segmentation process for further clinical analysis and diagnosis (example: Left Ventricular (LV) boundary detection and other 2D quantitative measurements)...
Image segmentation denotes a process of partitioning an image into distinct regions. A large variety of different segmentation approaches for images have been developed. Among them, the clustering methods have been extensively investigated and used. In this paper, a clustering based approach using a Self Organizing Map (SOM) algorithm is proposed for medical image segmentation. This paper describe...
This paper describes a spectroscopic approach for hyperspectral imaging of Plasmodium Falciparum infected human red blood cells (RBCs). We have performed a broad-band hyperspectral microscope which has been used to acquire a number of images in 370 nm to 1100 nm range, from fresh human RBCs infected by Plasmodium Falciparum. These images have been analyzed using the fast computation of entropies and...
It has been well documented that radiologists' performance is not perfect: they make both false positive and false negative decisions. For example, approximately thirty percent of early lung cancer is missed on chest radiographs when the evidence is clearly visible in retrospect. Currently computer-aided detection (CAD) uses software, designed to reduce errors by drawing radiologists' attention to...
This paper presents a modified FCM algorithm for segmentation of MRI. The proposed method has introduced by modifying the objective function of the standard FCM and it has the advantage that it can be applied at an early stage in an automated data analysis. The proposed method can deal with the intensity in-homogeneities and image noise effectively. have compared our results with other reported methods...
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