Curvelet and fast marching method‐based technique for efficient artifact detection and removal in dermoscopic images. Issue 4 (27th July 2021)

• Record Type: Journal Article
• Authors: Choudhary, Priya; Singhai, Jyoti; Yadav, J. S.
• Is Part Of: International journal of imaging systems and technology. Volume 31:Issue 4(2021)
• Page Start: 2334
• Page End: 2345
• Publication Date: 2021-07-27
• Subjects: curvelet -- fast marching method -- Frangi vesselness -- melanoma; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22633
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Effective detection and removal of the artifacts from dermoscopic images require the analysis of structures within lesions for accurate cancer detection. But the dermoscopic images acquired may be affected by image acquisition noise, variation in texture, color, and irrelevant structure such as hairs, air bubbles, and contour blurring around the lesion. Hence preprocessing is very essential for accurate diagnosis. The proposed algorithm includes the preprocessing, to remove acquisition noise using curvelet transform, illumination correction using contrast limited adaptive histogram equalization (CLAHE) algorithm for image enhancement, Frangi vesselness algorithm is utilized for detection and removal of hairs and vessels like structures present in enhanced image, fast marching inpainting method is applied for repairing the information of lesion from removed hair pixels. The performance of the proposed algorithm is analyzed based on commonly used parameters such as diagnostic accuracy (DA), sensitivity (SE), specificity (SP), and precision ( P ). The performance is evaluated on PH2 and International Symposium on Biomedical Imaging (ISBI) datasets than compared with existing methods. The proposed algorithm segments the skin lesion with the highest diagnostic accuracy of 81.49% and sensitivity of 93.88% for the PH2 dataset and 75.39% and 79.34% respectively for the ISBI dataset. Results are better than existing methods. The proposed algorithm with improved preprocessing for artifact detection and removal is highly accurate and able to retrieve the hair occluded information.