Truncate Threshold Segmentation of Skin Cancer using Computer Aided Diagnostic Tools

Skin cancer is one of the most prevalent cancer types. Skin cells proliferate in an uncontrolled manner due to the extensive exposure of UV light. In addition, doctors can use the cells to determine the type of skin cancer. With the help of imaging diagnostics, skin cancer can be detected and treated early, even if it doesn’t present many symptoms at first. In computer-aided diagnostic systems we cover the current state of the art and review existing methods, problems, and future plans for dermoscopic image acquisition, preprocessing, and segmentation. The automatic identification of melanoma using digital image processing is the focus of this research study. The image is divided into two categories: cancer lesions and normal skin using the derived feature parameters. The system aims to detect skin cancer at an early stage by analyzing images of lesions. One of the major obstacles to accurate segmentation in skin lesion analysis is hair removal from skin lesion images. This paper has the Black Hat filter, which recognizes and eliminates hair from dermoscopy images automatically. The crucial procedure of picture pre-processing has been used on the images that are being displayed. The pre-processing method used in this study was a bilateral filter. Image segmentation is used to separate regions of interest from the provided images. There are many segmentation methods available. This study used truncation threshold segmentation to separate the region of interest from the filtered image.