An image analysis approach for automatic malignancy determination of prostate pathological images.
BACKGROUND
Determining malignancy of prostate pathological samples is important for treatment planning of prostate cancer. Traditionally, this is performed by expert pathologists who evaluate the structure of prostate glands in the biopsy samples. However, this is a subjective task due to inter- and intra-observer differences among pathologists. Also, it is time-consuming and difficult to some extent. Therefore, automatic determination of malignancy of prostate pathological samples is of interest.


METHODS
A texture-based technique is first used to segment the prostate glands in the image. Features related to size and shape of these glands are then extracted and combined to generate an index, which is proportional to malignancy of cancer. A linear classifier is employed to classify the specimens into benign (low potential for malignancy) and malignant.


RESULTS
The leave-one-out technique is employed to evaluate the method using two datasets. The first has 91 images with similar magnifications and illuminations while the second has 199 images with different magnifications and illuminations. In the experiments, accuracies of about 98 and 95% have been achieved for these two datasets, respectively.


CONCLUSIONS
An image analysis approach is employed to evaluate prostate pathological images. Experimental results show that the proposed method can successfully classify the prostate biopsy samples into benign and malignant. They also show that the proposed method is robust to variations in magnification and illumination.
Farjam
Reza
R
Control and Intelligent Processing Center of Excellence, Department of Electrical and Computer Engineering, Faculty of Engineering, University of Tehran, Tehran 14395-515, Iran.
Soltanian-Zadeh
Hamid
H
Jafari-Khouzani
Kourosh
K
Zoroofi
Reza A
RA
D000465:Algorithms; D002277:Carcinoma / Q000145:classification / Q000473:pathology*; D002467:Cell Nucleus / Q000473:pathology; D048430:Cell Shape / Q000502:physiology; D003951:Diagnostic Errors / Q000517:prevention & control; D042241:Early Diagnosis; D004847:Epithelial Cells / Q000473:pathology; D006801:Humans; D019044:Image Cytometry / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008297:Male; D015588:Observer Variation; D011237:Predictive Value of Tests; D011467:Prostate / Q000473:pathology* / Q000503:physiopathology; D011471:Prostatic Neoplasms / Q000145:classification / Q000473:pathology*; D012984:Software / Q000639:trends*; D012986:Software Validation; D017154:Stromal Cells / Q000473:pathology