Early identification of melanocytic skin lesions increases the survival rate for skin cancer patients. Automated melanocytic skin lesion extraction from dermoscopic images using the computer vision approach is a challenging task as the lesions present in the image can be of different colors, there may be a variation of contrast near the lesion boundaries, lesions may have different sizes and shapes, etc. Therefore, lesion extraction from dermoscopic images is a fundamental step for automated melanoma identification. In this article, a watershed transform based on the fast fuzzy c-means (FCM) clustering algorithm is proposed for the extraction of melanocytic skin lesion from dermoscopic images. Initially, the proposed method removes the artifacts from the dermoscopic images and enhances the texture regions. Further, it is filtered using a Gaussian filter and a local variance filter to enhance the lesion boundary regions. Later, the watershed transform based on MMLVR (multiscale morphological local variance reconstruction) is introduced to acquire the superpixels of the image with accurate boundary regions. Finally, the fast FCM clustering technique is implemented in the superpixels of the image to attain the final lesion extraction result. The proposed method is tested in the three publicly available skin lesion image datasets, i.e., ISIC 2016, ISIC 2017 and ISIC 2018. Experimental evaluation shows that the proposed method achieves a good result.

Quantum key distribution (QKD) is a viable technique for safeguarding image transmission against numerous attacks. To address the limitations of existing methods in securing electronic medical records, we have explored an encryption technique using a set of real-time medical images along with existing standard images using QKD protocols including BB84 (Bennett-Brassard 1984), CASCADE (Cascading Error Correction Algorithm for Data Exchange), and differential-phase-shift (DPS) under the fiber-based (OFC) and free-space (FSO) channels. It utilizes the quantum bit-plane representation of the Real Ket (QBRK) model and a logistic chaotic system with XOR operation. The performance of the developed framework is tested via visual illustration and 11 different quantitative assessments. The average entropy of the scrambled image for the medical source image is 7.9896, which is quite near to the optimum value of 8. The UACI (Unified Averaged Changed Intensity) and the NPCR (Number of Pixels Change Rate) metrics values lie between 33.7879 to 33.9711 and 99.6014 to 99.8000 for the FSO channel. The BB84 QKD protocol achieves a QBER (Quantum Bit Error Rate) of 0.0028 for an FSO channel with DCR (Dark Count Rate), which improves to 0.0012 when DCR is ignored for a 10 km link range. In addition, we analyzed and compared the effect of OFC on system output as the quantum channel. From several experiments, it is observed that the BB84 provides better results against CASCADE and DPS. The proposed work is validated using the Optisystem 15.1 software under FSO and for OFC.