IEEE Transactions on Pattern Analysis and Machine Intelligence
The watershed transform: definitions, algorithms and parallelization strategies
Fundamenta Informaticae - Special issue on mathematical morphology
Computer Vision
Introduction to Algorithms
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Circularity of objects in images
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Computer-aided detection of breast cancer nuclei
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
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Accurate grading for hepatocellular carcinoma (HCC) in biopsy images is important to prognosis and treatment planning. However, visual grading is always time-consuming, subjective, and inconsistent. In this paper, we proposed a novel approach to automatically classifying biopsy images into five grades. At first, a dual morphological reconstruction method was applied to remove noise and accentuate nuclear shapes. Then we used watershed and snake techniques to smoothly segment nuclei from their background. Fourteen features were extracted according to six types of characteristics. We constructed a hierarchical classifier using Support Vector Machine and Sequential Floating Forward Selection method to automatically select an optimal set of features at each decision node of the classifier. Our experimental results demonstrated that 94.5% of accuracy can be achieved for a set of 604 biopsy images.