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Automatic detection of microcalcifications in mammograms constitutes a helpful tool in breast cancer diagnosis. Radiologist's confidence level on microcalcification detection would be improved if a probability estimate of its presence could be obtained from computer-aided diagnosis. In this paper we explore detection performance of a simple Bayesian classifier based on Gaussian mixture probability...
The use of two powerful classification techniques (boosting and SVM) is explored for the segmentation of white-matter lesions in the MRI scans of human brain. Simple features are generated from proton density (PD) scans. Radial basis function (RBF) based Adaboost technique and support vector machines (SVM) are employed for this task. The classifiers are trained on severe, moderate and mild cases....
In this paper, the main image processing methods used for both passive and active thermography are presented. 1st and 2nd order statistical thermal signatures are discussed. Typical methods of classification are presented
Thermography is a non-invasive and non-contact imaging technique used widely in the medical arena. This paper investigates the analysis of thermograms with the use of bio-statistical methods and artificial neural networks (ANN). It is desired that through these approaches, highly accurate diagnosis using thermography techniques can be established. The proposed advanced technique, is a multi-pronged...
We have been developing a computer-aided diagnosis (CAD) system for distinguishing the cirrhosis in MR images by shape and texture analysis. Two shape features are calculated from a segmented liver region, and seven texture features are quantified by using grey level difference method (GLDM) within the small region-of-interests (ROIs). The degree of cirrhosis is derived from integrating the shape...
In fMRI dataset, the population of actived voxels is always much less than the total population of the voxels, and that produced an ill-balanced dataset. Some methods, such as limiting the analysis to the gray matter voxels where the BOLD signal is expected and removing the voxels that is absolutely non-actived based on statistical criteria, have been used to treat the ill-balanced dataset. In this...
We have recently developed a sagittal laser optical tomographic (SLOT) imaging system for the diagnosis and monitoring of inflammatory processes in proximal interphalangeal (PIP) joints of patients with rheumatoid arthritis (RA). While cross sectional images of distribution of optical properties can now be generated easily, clinical interpretation of these images remains a challenge. In this paper,...
Support vector machine (SVM) can be seen as a new machine learning way which is based on the idea of VC dimensions and the principle of structural risk minimization rather than empirical risk minimization. SVM can be used for classification and regression. Support vector regression (SVR) is a very important branch of Support vector machine. Partial differential equations (PDEs) have been successfully...
We proposed an efficient method for classification of diffused liver diseases based on Gabor wavelet. It is well known that Gabor wavelets attain maximum joint space-frequency resolution which is highly significant in the process of texture extraction and presentation. This property has been explored here as the proposed method outperforms the classification rate obtained by using dyadic wavelets...
The aim of this paper was to investigate the usefulness of multiscale morphological analysis in the assessment of atherosclerotic carotid plagues. Ultrasound images were recorded from 137 asymptomatic and 137 symptomatic plaques and were converted to binary images at low, middle and high intensity intervals based on structural morphology . Low images represent low intensity regions corresponding to...
The classification of the uterine myoma and adenomyosis from their ultrasound images mainly depends on doctors' experience and lacks objective criterions. Here a novel classification method is proposed using the multiresolution analysis and the orientational fractal analysis. Firstly, texture features under various resolutions and orientational fractal features are obtained from ultrasound images...
In solving intra-voxel fiber crossing problem in white matter fiber tracking, the classification of crossing and non-crossing regions seems essential and challenging. Although high Cp value is a usable indicator of intra-voxel orientational heterogeneity, only using this metric is not accurate enough to decide exactly whether fiber crossing occurs as the lack of an exact and recognized threshold....
Masses due to benign breast diseases and tumors due to breast cancer present significantly different shapes on mammograms. In general, malignant tumors appear with rough and complex boundaries or contours, whereas benign masses present smooth, round, or oval contours. Fractal analysis may be used to derive shape features to perform pattern classification of breast masses and tumors. Several procedures...
In this study, a learning-based color image conversion method is proposed for cell image segmentation. Firstly, we demonstrate that minimum distance-based pixel classification, such as clustering, for color image segmentation in the color space is equivalent to thresholding grayscale images. Motivated by this result, we develop the so called C-G-T procedure for color image segmentation, where color...
Abdominal fat accumulation is an important cardiovascular risk factor. In clinical practice, delineation of subcutaneous and visceral fat is performed manually by an expert. This procedure is labor intensive, time consuming, and subject to inter- and intra-observer variability. In this paper, we present an extension of our previous work on automatic fat burden quantification and classification. Our...
A method for segmentation of detected masses in digital mammograms is introduced. The method is based on gray scale mathematical morphology. In a preprocessing step, image enhancement based on a local histogram technique is applied, followed by a morphological smoothing operation. The watershed transform is then applied to the gradient of the smoothed image resulting in segmented regions. A good segmentation...
Economic considerations make the conventional chest radiograph (X-ray) film an important ingredient in the diagnostic process. An initial clinical investigation for patients with suspected lung ailments is the study of the chest X-rays. The problem of detection for diseases in their early stages are well known using X-ray. A technique involving wavelets coefficient as the feature vector and Andrew's...
The objective of this study was to develop a standardized protocol for the capturing and analysis of endoscopy digital images for subsequent use in a computer aided diagnosis (CAD) system in gynaecological cancer. Images were captured at optimum illumination and focus at 720times576 pixels using 24 bits color in the following cases: (i) for a variety of testing targets from a color palette with known...
The detection and classification of leukocytes in blood smear images is a routine task in medical diagnosis. In this paper we present a fully automated approach to leukocyte segmentation that is robust with respect to cell appearance and image quality. A set of features is used to describe cytoplasm and nucleus properties. Pairwise SVM classification is used to discriminate between different cell...
This paper presents an approach for early breast cancer diagnostic by employing combination of artificial neural networks (ANN) and wavelet based subband image decomposition which detect microcalcification in digital mammograms. The microcalcifications correspond to high-frequency components of the image spectrum, detection of microcalcifications is achieved by decomposing the mammograms into different...
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