TY - JOUR
T1 - Computer-Aided Prostate Cancer Diagnosis from Digitized Histopathology
T2 - A Review on Texture-Based Systems
AU - Mosquera-Lopez, Clara
AU - Agaian, Sos
AU - Velez-Hoyos, Alejandro
AU - Thompson, Ian
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015
Y1 - 2015
N2 - Prostate cancer (PCa) is currently diagnosed by microscopic evaluation of biopsy samples. Since tissue assessment heavily relies on the pathologists level of expertise and interpretation criteria, it is still a subjective process with high intra-and interobserver variabilities. Computer-aided diagnosis (CAD) may have a major impact on detection and grading of PCa by reducing the pathologists reading time, and increasing the accuracy and reproducibility of diagnosis outcomes. However, the complexity of the prostatic tissue and the large volumes of data generated by biopsy procedures make the development of CAD systems for PCa a challenging task. The problem of automated diagnosis of prostatic carcinoma from histopathology has received a lot of attention. As a result, a number of CAD systems, have been proposed for quantitative image analysis and classification. This review aims at providing a detailed description of selected literature in the field of CAD of PCa, emphasizing the role of texture analysis methods in tissue description. It includes a review of image analysis tools for image preprocessing, feature extraction, classification, and validation techniques used in PCa detection and grading, as well as future directions in pursuit of better texture-based CAD systems.
AB - Prostate cancer (PCa) is currently diagnosed by microscopic evaluation of biopsy samples. Since tissue assessment heavily relies on the pathologists level of expertise and interpretation criteria, it is still a subjective process with high intra-and interobserver variabilities. Computer-aided diagnosis (CAD) may have a major impact on detection and grading of PCa by reducing the pathologists reading time, and increasing the accuracy and reproducibility of diagnosis outcomes. However, the complexity of the prostatic tissue and the large volumes of data generated by biopsy procedures make the development of CAD systems for PCa a challenging task. The problem of automated diagnosis of prostatic carcinoma from histopathology has received a lot of attention. As a result, a number of CAD systems, have been proposed for quantitative image analysis and classification. This review aims at providing a detailed description of selected literature in the field of CAD of PCa, emphasizing the role of texture analysis methods in tissue description. It includes a review of image analysis tools for image preprocessing, feature extraction, classification, and validation techniques used in PCa detection and grading, as well as future directions in pursuit of better texture-based CAD systems.
KW - Computer-aided diagnosis (CAD)
KW - gleason grading
KW - histopathology image analysis
KW - pattern recognition
KW - prostate cancer
KW - texture-based CAD systems
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U2 - 10.1109/RBME.2014.2340401
DO - 10.1109/RBME.2014.2340401
M3 - Article
C2 - 25055385
AN - SCOPUS:84940027097
SN - 1937-3333
VL - 8
SP - 98
EP - 113
JO - IEEE Reviews in Biomedical Engineering
JF - IEEE Reviews in Biomedical Engineering
M1 - 6857992
ER -