It takes the average reader 3 hours and 10 minutes to read Computerized Analysis of Mammographic Images for Detection and Characterization of Breast Cancer by Paola Casti
Assuming a reading speed of 250 words per minute. Learn more
The identification and interpretation of the signs of breast cancer in mammographic images from screening programs can be very difficult due to the subtle and diversified appearance of breast disease. This book presents new image processing and pattern recognition techniques for computer-aided detection and diagnosis of breast cancer in its various forms. The main goals are: (1) the identification of bilateral asymmetry as an early sign of breast disease which is not detectable by other existing approaches; and (2) the detection and classification of masses and regions of architectural distortion, as benign lesions or malignant tumors, in a unified framework that does not require accurate extraction of the contours of the lesions. The innovative aspects of the work include the design and validation of landmarking algorithms, automatic Tabár masking procedures, and various feature descriptors for quantification of similarity and for contour independent classification of mammographic lesions. Characterization of breast tissue patterns is achieved by means of multidirectional Gabor filters. For the classification tasks, pattern recognition strategies, including Fisher linear discriminant analysis, Bayesian classifiers, support vector machines, and neural networks are applied using automatic selection of features and cross-validation techniques. Computer-aided detection of bilateral asymmetry resulted in accuracy up to 0.94, with sensitivity and specificity of 1 and 0.88, respectively. Computer-aided diagnosis of automatically detected lesions provided sensitivity of detection of malignant tumors in the range of [0.70, 0.81] at a range of falsely detected tumors of [0.82, 3.47] per image. The techniques presented in this work are effective in detecting and characterizing various mammographic signs of breast disease.
Computerized Analysis of Mammographic Images for Detection and Characterization of Breast Cancer by Paola Casti is 186 pages long, and a total of 47,616 words.
This makes it 63% the length of the average book. It also has 58% more words than the average book.
The average oral reading speed is 183 words per minute. This means it takes 4 hours and 20 minutes to read Computerized Analysis of Mammographic Images for Detection and Characterization of Breast Cancer aloud.
Computerized Analysis of Mammographic Images for Detection and Characterization of Breast Cancer is suitable for students ages 10 and up.
Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.
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