12,366 research outputs found
Histopathological image analysis : a review
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe
An Efficient Automatic Mass Classification Method In Digitized Mammograms Using Artificial Neural Network
In this paper we present an efficient computer aided mass classification
method in digitized mammograms using Artificial Neural Network (ANN), which
performs benign-malignant classification on region of interest (ROI) that
contains mass. One of the major mammographic characteristics for mass
classification is texture. ANN exploits this important factor to classify the
mass into benign or malignant. The statistical textural features used in
characterizing the masses are mean, standard deviation, entropy, skewness,
kurtosis and uniformity. The main aim of the method is to increase the
effectiveness and efficiency of the classification process in an objective
manner to reduce the numbers of false-positive of malignancies. Three layers
artificial neural network (ANN) with seven features was proposed for
classifying the marked regions into benign and malignant and 90.91% sensitivity
and 83.87% specificity is achieved that is very much promising compare to the
radiologist's sensitivity 75%.Comment: 13 pages, 10 figure
Automated System for Early Breast Cancer Detection in Mammograms
The increasing demand on mammographic screening for early breast cancer detection, and the subtlety of early breast cancer signs on mammograms, suggest an automated image processing system that can serve as a diagnostic aid in radiology clinics. We present a fully automated algorithm for detecting clusters of microcalcifications that are the most common signs of early, potentially curable breast cancer. By using the contour map of the mammogram, the algorithm circumvents some of the difficulties encountered with standard image processing methods. The clinical implementation of an automated instrument based on this algorithm is also discussed
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