Automatic classification for pathological prostate images based on fractal analysis.
Accurate grading for prostatic carcinoma in pathological images is important to prognosis and treatment planning. Since human grading is always time-consuming and subjective, this paper presents a computer-aided system to automatically grade pathological images according to Gleason grading system which is the most widespread method for histological grading of prostate tissues. We proposed two feature extraction methods based on fractal dimension to analyze variations of intensity and texture complexity in regions of interest. Each image can be classified into an appropriate grade by using Bayesian, k-NN, and support vector machine (SVM) classifiers, respectively. Leave-one-out and k-fold cross-validation procedures were used to estimate the correct classification rates (CCR). Experimental results show that 91.2%, 93.7%, and 93.7% CCR can be achieved by Bayesian, k-NN, and SVM classifiers, respectively, for a set of 205 pathological prostate images. If our fractal-based feature set is optimized by the sequential floating forward selection method, the CCR can be promoted up to 94.6%, 94.2%, and 94.6%, respectively, using each of the above three classifiers. Experimental results also show that our feature set is better than the feature sets extracted from multiwavelets, Gabor filters, and gray-level co-occurrence matrix methods because it has a much smaller size and still keeps the most powerful discriminating capability in grading prostate images.
Huang
Po-Whei
PW
Department of Computer Science and Engineering,National Chung Hsing University, Taichung 40227, Taiwan. powhei.huang@msa.hinet.net
Lee
Cheng-Hsiung
CH
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D017709:Fractals; D006651:Histocytochemistry / Q000379:methods; D006801:Humans; D008297:Male; D008853:Microscopy; D009367:Neoplasm Staging; D010363:Pattern Recognition, Automated / Q000379:methods*; D011471:Prostatic Neoplasms / Q000473:pathology*; D015203:Reproducibility of Results