Performance evaluation of maximal separation techniques in immunohistochemical scoring of tissue images

• Published in: Micron (Oxford, England : 1993) England : 1993), 2015-12, Vol.79, p.29-35
• Main Authors: Hameed, K.A. Shahul, Banumathi, A., Ulaganathan, G.
• Format: Article
• Language: English
• Subjects: Algorithms; Automatic scoring; Automation; Cell Nucleus - ultrastructure; Color; Entropy; Global entropy; Gray-level co-occurrence matrix; Humans; Image Processing, Computer-Assisted - methods; Immunohistochemistry - methods; Mouth Neoplasms - pathology; Mouth Neoplasms - ultrastructure; p53 immunostain; Thresholding; Tumor Suppressor Protein p53 - analysis; Watershed transform
• ISSN: 0968-4328; 1878-4291
• DOI: 10.1016/j.micron.2015.07.013

•A simple automatic IHC scoring technique is presented.•Representative regions of tissue images are segmented using entropy thresholding method.•Clustered cells are resolved by selectively applying watershed transform.•Maximal separation techniques are employed to classify each cell nuclei into one of four categories.•IHC scoring based on blue component of tissue image has high potential to help the pathologist during image analysis. This paper presents an automatic scoring method for p53 immunostained tissue images of oral cancer that consist of tissue image segmentation, splitting of clustered nuclei, feature extraction and classification. The tissue images are segmented using entropy thresholding technique in which the optimum threshold value to each color component is obtained by maximizing the global entropy of its gray-level co-occurrence matrix and clustered cells are separated by selectively applying marker-controlled watershed transform. Cell nuclei feature is extracted by maximal separation technique (MS) based on blue component of tissue image and subsequently, each cell is classified into one of four categories using multi-level thresholding. Finally, IHC score of tissue images have been determined using Allred method. A statistical analysis is performed between immuno-score of manual and automatic method, and compared with the scores that have obtained using other MS techniques. According to the performance evaluation, IHC score based on blue component that has high correlation coefficients (CC) of 0.95, low mean difference (MD) of 0.15, and a very close range of 95% confidence interval with manual scores. Therefore, automatic scoring method presented in this paper has high potential to help the pathologist in IHC scoring of tissue images.