Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network. (June 2018)

• Record Type: Journal Article
• Authors: Pal, Anabik; Garain, Utpal; Chandra, Aditi; Chatterjee, Raghunath; Senapati, Swapan
• Is Part Of: Computer methods and programs in biomedicine. Volume 159(2018)
• Page Start: 59
• Page End: 69
• Publication Date: 2018-06
• Subjects: Psoriasis Biopsy image -- Dermis-Epidermis -- Simple Linear Iterative Clustering (SLIC) -- Deep Convolutional Neural Network (DCNN) -- Fully Convolutional Neural Network (FCN) -- Data set and Evaluation; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.01.027
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Analysis of Deep Convolutional Neural Network (DCNN) based classifiers for segmenting psoriasis affected human skin biopsy images into dermis, epidermis and non-tissue regions. A comparative study is presented to bring out the viability of the proposed approach. An U-Net architecture based semantic segmentation is also experimented. Development of a human psoriasis skin biopsy image dataset of ninety (90) images having dermis and epidermis segmentation ground truth. Abstract: Background and objective: Development of machine assisted tools for automatic analysis of psoriasis skin biopsy image plays an important role in clinical assistance. Development of automatic approach for accurate segmentation of psoriasis skin biopsy image is the initial prerequisite for developing such system. However, the complex cellular structure, presence of imaging artifacts, uneven staining variation make the task challenging. This paper presents a pioneering attempt for automatic segmentation of psoriasis skin biopsy images. Methods: Several deep neural architectures are tried for segmenting psoriasis skin biopsy images. Deep models are used for classifying the super-pixels generated by Simple Linear Iterative Clustering (SLIC) and the segmentation performance of these architectures is compared with the traditional hand-crafted feature based classifiers built on popularly used classifiers like K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). A U-shaped Fully Convolutional Neural Network (FCN) is also used in an end to end learning fashion where input is the original color image and the output is the segmentation class map for the skin layers. Results: An annotated real psoriasis skin biopsy image data set of ninety (90) images is developed and used for this research. The segmentation performance is evaluated with two metrics namely, Jaccard's Coefficient (JC) and the Ratio of Correct Pixel Classification (RCPC) accuracy. The experimental results show that the CNN based approaches outperform the traditional hand-crafted feature based classification approaches. Conclusions: The present research shows that practical system can be developed for machine assisted analysis of psoriasis disease.