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From the preoperative partial nephrectomy planning perspective, it is essential to expose separately different kidney structures and to analyze their mutual topological relations. Only then, the identification of possible conflicts prior to surgical intervention can be facilitated. To enable this, we propose a segmentation frameworks for renal vascular tree, kidney and pelvicalyceal system from corresponding...
Micro-tomography produces high resolution images of biological structures such as vascular networks. In this paper, we present a new approach for segmenting vascular network into pathological and normal regions from considering their micro-vessel 3D structure only. We define and use a conditional random field for segmenting the output of a watershed algorithm. The tumoral and normal classes are thus...
In this paper a fully automatic method for segmenting MR images showing tumor, both mass-effect and infiltrating structures is presented. The proposed method uses UDWT and gabor wavelets. The proposed method uses T1, T2 images and produces appreciative results even in the presence of noise. A multiresolution approach using undecimated wavelet transform is employed which allows the low-low (LL), low-high...
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...
This paper presents a new approach based on modified adaptive probabilistic neural network for brain segmentation with magnetic resonance imaging (MRI). The SOM (Self-Organizing Map) neural network is employed to overly segment the input MR image, and yield reference vectors with a large training data set for the probabilistic classification. For improving the training quality of neural work, the...
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...
Automated segmentation of pigmented skin lesions (PSLs) from dermoscopy images is an important step for computer-aided diagnosis of skin cancer. The segmentation task involves classifying each image pixel as either lesion or skin. It is challenging because lesion and skin can often have similar appearance. We present a novel exemplar-based algorithm for lesion segmentation which leverages the context...
Lobewise analysis of the pulmonary parenchyma is of clinical relevance for diagnosing and monitoring pathologies. In this work, a fully automatic lobe segmentation approach is presented, which is based on a previously proposed watershed transformation approach. The proposed extension explicitly considers the pulmonary fissures by including them in the cost image for the watershed segmentation. The...
We consider the problem of detecting the presence of pneumoconiosis in a patient on the basis of evidence found in chest radiographs. Abnormalities pertaining to pneumoconiosis appear in the form of opacities of various sizes; the profusion of these opacities determines the stage of the disease. We present a multiresolution approach whereby we segment regions of interest (ROIs) from the X-Ray image...
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...
In this paper, a new algorithm for MRI Brain Segmentation is proposed, which is based on the AntPart algorithm [1]. This algorithm proposed partitiones the brain structure into three parts-white matter, grey matter, and cerebrospinal fluid according to the grayvalues of pixels. The main algorithm compares each pixel with the nearest class center C, all the data belonging to class C, all the data carried...
This study focuses on segmentation and validation of brain MR images. Artificial neural network (ANN) has been applied to obtain the targeted segments from these images. In preprocessing step for avoiding the chances of misclassification during training of ANN, the unwanted skull tissues were removed by employing active contour modeling (ACM). The removal of these tissues leaves an image containing...
Identification and characterization of diffuse parenchyma lung disease patterns challenges computer aided diagnosis (CAD) schemes in computed tomography (CT). Accuracy of these preprocessing stages is expected to influence the accuracy of lung CAD schemes. Although algorithms aimed at improving the accuracy of segmentation of lung fields in presence of DPLDs have been reported, the corresponding vessel...
In this paper, we propose a fully automatic method to segment bone compartments in magnetic resonance (MR) images of knee joints gathered from a public database for research on knee osteoarthritis (OA), the osteoarthritis initiative (OAI). Considering the fixed scanning parameters which include position and flexion of the knee joint, the proposed method efficiently utilizes both shape and intensity...
A novel method for the segmentation of brain structures combining registration-based and EM-based approaches is proposed. To address the issue of intensity variation within brain structures, we propose a method for creating spatial prior corresponding very well with the new subject anatomy, referred to as subject-specific atlas. The subject-specific atlas is created using multiple non-rigid registrations...
Various studies have been conducted to expand the utilization of combined positron emission tomography and computed tomography (PET/CT) covering cases of infection and inflammation. PET images provide the functional activity of a lesion while CT images demonstrate the anatomical location. Hence, existence of infected lesions can be recognized in PET image but since the structural position can not...
In this work, we consider a statistical approach to the fully automatic segmentation of heart walls from magnetic resonance imaging (MRI) data and the reconstruction of three-dimensional models from other modalities. The method is based on utilizing a Markov random fields (MRF) model that provides powerful opportunities for noise suppressing and, at the same time, for an accurate contour preserving...
This article presents an original method for elastic registration of 3d mesh based on multi resolution balanced Reeb graphs having a strong tolerance on the variation of installation and a mesh resolution. Maxilla-facial malformations are difficult diseases to be detected. The human cranium is composed of 29 bones. This difficulty is translated in the medical images by a difficult on segmentation...
With the development of medical treatment, various kinds of medical images become more and more important. Most of them are multi-valued image. Although the hierarchical structural image representation methods have many merits, they put too much emphasis upon the symmetry of segmentation. Therefore, they are not the optimal image representation methods. With the concept of packing problem, a dNAM-based...
Image segmentation is an essential technique in image analysis. In spite of issues in contour initialization, boundary concavities and high-level computation, active contour models, also known as snake, are a popular and successful method for segmentation among researchers. Segmentation process in snakes consists of calculation of energy and deformation of contour. In this paper, we present a new...
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