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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...
Introduction of automated methods for heart function assessment have the potential to minimize the variance in operator assessment. This paper considers automated classification of rest and stress echocardiography. One previous attempt has been made to combine information from rest and stress sequences utilizing a Hidden Markov Model (HMM), which has proven to be the best performing approach to date...
This paper proposes a non-intrusive method to predict/estimate the intracranial pressure (ICP) level based on features extracted from multiple sources. Specifically, these features include midline shift measurement and texture features extracted from CT slices, as well as patient's demographic information, such as age. Injury Severity Score is also considered. After aggregating features from slices,...
This paper presents a wavelet-based texture analysis method for classification of melanoma. The method applies tree-structured wavelet transform on different color channels of red, green, blue and luminance of dermoscopy images, and employs various statistical measures and ratios on wavelet coefficients. Feature extraction and a two-stage feature selection method, based on entropy and correlation,...
Mammography is probably the best method for early detection of abnormalities in the breast tissue. Higher breast tissue densities significantly reduce the overall detection sensitivity and can lead to false negative results. In automatic detection algorithms, knowledge about breast density can also be useful for setting an appropriate threshold. It is impossible to produce satisfactory classification...
wireless capsule endoscopy (WCE) is an important device to detect abnormalities in small intestine. Despite emerging technologies, reviewing capsule endoscopic video is a labor intensive task and very time consuming. Computational tools which automatically detect informative frames and tag abnormal conditions such as bleeding, ulcer or tumor will dramatically reduce the clinician's effort. In this...
The clinical interpretation of breast MRI remains largely subjective, and the reported findings qualitative. Although the sensitivity of the method for detecting breast cancer is high, its specificity is poor. Computerised interpretation offers the possibility of improving specificity through objective quantitative measurement. This paper reviews the plethora of such features that have been proposed...
Autofluorescence bronchoscopy (AFB) has been utilized over the past decade, proving to be a powerful tool for the detection and localization of premalignant and malignant lesions of the airways. AFB is, however, characterized by low specificity and a high rate of false positive findings (FPFs). The majority of FPFs are due to inflammations, as they often fluoresce at the same wavelengths with cancer...
Machine learning is a powerful paradigm to analyze proton magnetic resonance spectroscopy 1H-MRS spectral data for the classification of brain tumor pathologies. An important characteristic of this task is the high dimensionality of the involved data sets. In this work we apply filter feature selection methods on three types of 1H-MRS spectral data: long echo time, short echo time and an ad hoc combination...
The objective of this study is to investigate the use of pattern classification methods for distinguishing different types of brain tumors, such as primary gliomas from metastases, and also for grading of gliomas. A computer-assisted classification method combining conventional magnetic resonance imaging (MRI) and perfusion MRI is developed and used for differential diagnosis. The proposed scheme...
This research describes a non-interactive process that applies several forms of computational intelligence to the task of classifying biopsy lung tissue samples based on visual data in the form of raw digital photographs of those samples. The three types of lung cancer evaluated (squamous cell carcinoma, adenocarcinoma, and bronchioalveolar carcinoma) together account for 65-70% of lung cancer diagnoses...
Ultrasound Medical Imaging is currently the most popular modality for diagnostic application. This imaging technique has been used for the detecting abnormalities associated with abdominal organs like liver, kidney, uterus etc. In this paper, the possibilities of automatic classification of the ultrasound liver images into four classes-normal, cyst, benign and malignant masses, using texture features...
Ultrasound imaging has found its own place in medical applications as an effective diagnostic tool. Ultrasonic diagnostics has made possible the detection of cysts, tumors or cancers in abdominal organs. In this paper, the possibilities of an automatic classification of ultrasonic liver images by optimal selection of texture features are explored. These features are used to classify these images into...
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...
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