A novel and robust Bayesian approach for segmentation of psoriasis lesions and its risk stratification
Introduction
Psoriasis is an incurable skin disease but controllable by persistent and vigilant medication [1]. It is characterized as a reddish lesion covered by silvery-white scales on the skin surface due to faster production of skin cells than normal [2]. The cause of this disease is unidentified yet, but genetics is believed to be the leading reason. According to statistics, it affects over 125 million individuals globally [3]. However, its dominance varies geographically such as in Europe, USA, Malaysia and India, it is about 0.6–6.5% [4], 3.15% [4], 3% [5] and 1.02% [6], respectively. Besides affecting the skin, it also affects the quality of life because of its embarrassing physical appearance [7]. Its consequence includes more risk of attempting suicide and is reported to be about 30% and is comparable to life-threatening diseases such as heart disease, diabetes and depression [8]. Psoriasis is categorized as plaque, guttate, inverse, pustular, and erythrodermic based on distinct characteristics. Since plaque psoriasis is most frequently appearing (about 80%) [9], the database considered in this study was affected with plaque psoriasis. A sample of plaque psoriasis lesions is shown in Fig. 1.
Dermatologists are mainly concerned about level of severity of psoriasis disease for prescription of better medication better medication. Currently, dermatologists follow subjective assessment by visual and haptic inspection and accuracy of which depends on the experience and gained proficiency by the dermatologist. Further, inter- and intra- observer variability issue makes the subjective assessment inefficient and unreliable [10]. Hence, we here present a psoriasis risk assessment system (pRAS) that automatically segments the lesions and stratifies the severity of psoriasis.
The machine learning protocol has been adapted in literature for stratification of different dermatology diseases such as melanoma [11], [12], Erythemato-squamous diseases [13], [14] and dermatological ulcer [15]. However, machine learning protocol has been adapted recently for stratification of psoriasis images [16], [17], [18], [19], [20], [21]. The issue with the current psoriasis risk assessment systems is the absence of automatic segmentation of psoriatic lesion. Further, since the images have fuzzy characteristics due to the nature of the disease, it thus needs a classification algorithm which can learn different classes and predict the segmentation regions. We therefore, in this paper present a Bayesian approach for an automatic psoriatic lesion segmentation followed by lesion characterization, stratification and risk severity. These segmented images are then used as inputs for pRAS system for stratification of psoriasis severity. Furthermore, a typical risk stratification system involves three key modules: namely feature extraction, feature selection and classification. Feature selection is a crucial step in the design of risk stratification system and it even becomes more important with rising number of features. Secondly, since different classifiers perform differently based on the database, selection of suitable classifier is also an essential criterion to improve the performance of risk stratification system. Thus, we have designed nine different kinds of pRAS systems using combination of these key blocks and in-depth comparative analyses of their performances have been presented. Thus, the novelties of this paper are: (i) An automatic segmentation of psoriatic lesion using Bayesian model. (ii) Multiclass pRAS system for stratification of psoriasis severity. (iii) Design and development of nine pRAS systems that crisscrosses the three different classifiers and three feature selection techniques. The nine kinds of pRAS systems are: pRAS1: SVM-PCA; pRAS2: SVM-FDR; pRAS3: SVM-MI; pRAS4: DT-PCA; pRAS5: DT-FDR; pRAS6: DT-MI; pRAS7: NN-PCA; pRAS8: NN-FDR; and pRAS9: NN-MI.
Section snippets
Methodology: multiclass framework
The fundamental building block in this novel pRAS design is the automatic lesion segmentation based on Bayesian model. The lesion characterization followed by risk assessment is used a machine learning paradigm embedded with color and grayscale transformation and inter-combination of blocks like feature extraction, selection and cross-validation for risk prediction and performance evaluation.
The proposed pRAS system is shown in Fig. 2. The dotted line divides the system in two segments: offline
Results
The result section has been split-up into two parts. First part deals with the results of proposed automatic Bayesian segmentation approach. Visual as well as quantitative evaluation of segmentation approach has been presented to show the accuracy of our segmentation technique. The second part discusses the results of nine pRAS systems.
Our system
This paper presents risk stratification system to stratify psoriasis images into five classes namely: healthy, mild, moderate, severe and very severe. Moreover, nine pRAS systems have been designed by criss-cross combination of three classifiers (SVM, DT and NN) and three feature selection techniques (PCA, FDR and MI) and their performances have been compared. The automatic psoriatic lesion segmentation approach was developed as part of the automated risk assessment system. We validated the
Conclusion
The paper presented a complete automatic system for psoriasis disease severity assessment in multi-class scenario which combines segmentation and risk stratification. The main contribution is automatic segmentation of psoriatic lesion using Bayesian approach and development of nine risk stratification system utilizing three classifiers and three feature selection techniques. A comprehensive analysis among these nine pRAS systems has been established.
The combination of SVM classifier and FDR
Declaration of conflicting interests
None declared.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this paper.
Vimal K. Shrivastava has received his BE degree in Electronics and Telecommunication engineering from the Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh in 2009 and MTech degree in Electronics Instrumentation engineering from National Institute of Technology Warangal, Andhra Pradesh in 2011. Later, he received his PhD degree from National Institute of Technology, Raipur, India in 2016. He is currently working as Assistant Professor in School of Electronics Engineering
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2022, Computers in Biology and MedicineCitation Excerpt :Another weakness is that the number of risk factors or covariates present in the dataset was only 22. This was enough for preparation, predictions, and evaluation of the model; however, we could exercise the feature effect power much better with a larger number of features [16,26,93]. Even though we used the ADASYN paradigm for augmenting the imbalanced classes, various other possibilities exist to compare ADASYN models.
Vimal K. Shrivastava has received his BE degree in Electronics and Telecommunication engineering from the Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh in 2009 and MTech degree in Electronics Instrumentation engineering from National Institute of Technology Warangal, Andhra Pradesh in 2011. Later, he received his PhD degree from National Institute of Technology, Raipur, India in 2016. He is currently working as Assistant Professor in School of Electronics Engineering of Kalinga Institute of Industrial Technology, Bhubaneswar, India.
Narendra D. Londhe received his BE degree from Amravati University in 2000. Later he received his MTech and PhD degrees in the year 2004 and 2011, respectively from Indian Institute of Technology Roorkee. He is presently working as Assistant Professor in Department of Electrical Engineering of National Institute of Technology, Raipur, India.
Rajendra S. Sonawane is graduated from National Institute of Homeopathy, Kolkata, India. Later he received his M.D. and D.I. in homeopathy from London. He has treated over 15,000 psoriasis patients personally from all over India and many countries of the world since 27 years with homeopathic medicines only. He is the ex-professor of Homeopathic Medical Colleges, Malkapur, Amravati, Dhule and Shirpur.
Jasjit S. Suri, PhD, MBA, Fellow AIMBE is an innovator, visionary, scientist and an internationally known world leader. Dr. Suri received the Director General's Gold medal in 1980 and the Fellow of American Institute of Medical and Biological Engineering, awarded by National Academy of Sciences, Washington DC in 2004. He has published over 500 peer reviewed articles (H-index ∼ 42) and book chapters and over 100 innovations/trademarks. He is currently Chairman of Global Biomedical Technologies, Inc., Roseville, CA.