Classification of breast mass abnormalities using denseness and architectural distortion

This paper presents an electronic second opinion system for the classification of mass abnormalities in mammograms into benign and malignant categories. This system is designed to help radiologists to reduce the number of benign breast cancer biopsies. Once a mass abnormality is detected and marked on a mammogram by a radiologist, two textural features, named denseness and architectural distortion, are extracted from the marked area. The denseness feature provides a measure of radiographic denseness of the marked area, whereas the architectural distortion feature provides a measure of its irregularity. These features are then fed into a neural network classifier. Receiver operating characteristic (ROC) analysis was conducted to evaluate the system performance. The area under the ROC curve reached 0.90 for the DDSM database consisting of 404 biopsy proven masses. A sensitivity analysis was also performed to examine the robustness of the introduced texture features to variations in sizes of abnormality markings

Characterization of skin lesion texture in diffuse reflectance spectroscopic images

This paper examines various texture features extracted from skin lesion images obtained by using diffuse reflectance spectroscopic imaging. Different image texture features have been applied to such images to separate precancerous from benign cases. These features are extracted based on the co-occurrence matrix, wavelet decomposition , fractal signature, and granulometric approaches. The results so far indicate that fractal and wavelet-based features are effective in distinguishing precancerous from benign cases

Skin cancer detection by spectroscopic oblique-incidence reflectometry: classification and physiological origins

Data obtained from 102 skin lesions in vivo by spectroscopic oblique-incidence reflectometry were analyzed. The participating physicians initially divided the skin lesions into two visually distinguishable groups based on the lesions’ melanocytic conditions. Group 1 consisted of the following two cancerous and benign subgroups: (1) basal cell carcinomas and squamous cell carcinomas and (2) benign actinic keratoses, seborrheic keratoses, and warts. Group 2 consisted of (1) dysplastic nevi and (2) benign common nevi. For each group, a bootstrap-based Bayes classifier was designed to separate the benign from the dysplastic or cancerous tissues. A genetic algorithm was then used to obtain the most effective combination of spatiospectral features for each classifier. The classifiers, tested with prospective blind studies, reached statistical accuracies of 100% and 95% for groups 1 and 2, respectively. Properties that related to cell-nuclear size, to the concentration of oxyhemoglobin, and to the concentration of deoxyhemoglobin as well as the derived concentration of total hemoglobin and oxygen saturation were defined to explain the origins of the classification outcomes

Skin lesion classification using oblique-incidence diffuse reflectance spectroscopic imaging

We discuss the use of a noninvasive in vivo optical technique, diffuse reflectance spectroscopic imaging with oblique incidence, to distinguish between benign and cancer-prone skin lesions. Various image features were examined to classify the images from lesions into benign and cancerous categories. Two groups of lesions were processed separately: Group 1 includes keratoses, warts versus carcinomas; and group 2 includes common nevi versus dysplastic nevi. A region search algorithm was developed to extract both one- and two-dimensional spectral information. A bootstrap-based Bayes classifier was used for classification. A computer-assisted tool was then devised to act as an electronic second opinion to the dermatologist. Our approach generated only one false-positive misclassification out of 23 cases collected for group 1 and two misclassifications out of 34 cases collected for group 2 under the worst estimation condition

Efficient Personalization of Amplification in Hearing Aids via Multi-band Bayesian Machine Learning

Personalization of the amplification function of hearing aids has been shown to be of benefit to hearing aid users in previous studies. Several machine learning-based personalization approaches have been introduced in the literature. This paper presents a machine learning personalization approach with the advantage of being efficient in its training based on paired comparisons which makes it practical and field deployable. The training efficiency of this approach is the result of treating frequency bands independent of one another and by simultaneously carrying out Bayesian machine learning in each band across all of the frequency bands. Simulation results indicate that this approach leads to an estimated hearing preference function close to the true hearing preference function in fewer number of paired comparisons relative to the previous machine learning approaches. In addition, a clinical experiment conducted on eight subjects with hearing impairment indicate that this training efficient personalization approach provides personalized gain settings which are on average six times more preferred over the standard prescriptive gain settings