-Score-Based Secure Biomedical Model for Effective Skin Lesion Segmentation Over eHealth Cloud

Abstract

This study aims to process the private medical data over eHealth cloud platform. The current pandemic situation, caused by Covid19 has made us to realize the importance of automatic remotely operated independent services, such as cloud. However, the cloud servers are developed and maintained by third parties, and may access user’s data for certain benefits. Considering these problems, we propose a specialized method such that the patient’s rights and changes in medical treatment can be preserved. The problem arising due to Melanoma skin cancer is carefully considered and a privacy-preserving cloud-based approach is proposed to achieve effective skin lesion segmentation. The work is accomplished by the development of a Z -score-based local color correction method to differentiate image pixels from ambiguity, resulting the segmentation quality to be highly improved. On the other hand, the privacy is assured by partially order homomorphic Permutation Ordered Binary (POB) number system and image permutation. Experiments are performed over publicly available images from the ISIC 2016 and 2017 challenges, as well as PH dataset, where the proposed approach is found to achieve significant results over the encrypted images (known as encrypted domain), as compared to the existing schemes in the plain domain (unencrypted images). We also compare the results with the winners of the ISBI 2016 and 2017 challenges, and show that the proposed approach achieves a very close result with them, even after processing test images in the encrypted domain. Security of the proposed approach is analyzed using a challenge-response game model.

Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

“…The evaluation metrics of accuracy, sensitivity, specificity, dice similarity, and running time are commonly used for evaluating the performance of a segmentation method for skin lesions [ 10 , 12 , 23 , 30 , 35 , 37 , 52 , 59 ]. Sensitivity is the amount of the correctly detected pixels of skin lesions while specificity is the ratio of the correctly segmented non-lesion pixels [ 10 , 70 , 71 ].…”

“…The CLAHE is widely recognized as the best method among the prevailing enhancement methods for preprocessing of medical images [ 40 ]. In addition, literature has shown evidence of preprocessing stages based on histogram [ 33 ], mean subtraction [ 31 ], deep learning [ 34 ], multiscale decomposition [ 21 ], adaptive gamma correction [ 23 ], Z-score transformation [ 52 ], and Frangi Vesselness filter [ 41 ]. The artifact removal and image enhancement algorithms are generally executed before the actual segmentation and postprocessing methods are applied to suppress the leftover noise.…”

Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey

“…It provides a boost to the sensitivity of the information in images and presents enhanced input to carry out other procedures. In skin cancer recognition the research utilized an anisotropic diffusion filter [ 34 , 35 ], median filter [ 35 , 36 ], Dull Razor [ 37 , 38 , 39 , 40 ], Adam Huang algorithm [ 41 ], Wiener filter [ 42 ], Bilateral filter [ 43 ] and Gaussian and Gaussian Blur filter [ 44 , 45 ], Z-score transformation [ 46 ], contrast-limited adaptive histogram equalization [ 47 , 48 ], adaptive histogram equalization [ 49 ], global-local contrast stretching [ 50 ], color constancy with shades of gray [ 51 ], adaptive gamma correction [ 52 ], gamma and correction [ 53 ], etc., for enhancement and noise removal in the skin recognition process.…”