A Comprehensive Approach to Image Disease Detection: Combining Image Segmentation, Feature Extraction, Using MATLAB

Skin diseases are among the most common health problems worldwide, affecting millions of people regardless of age or ethnicity. Early and accurate detection of skin abnormalities is crucial for effective diagnosis and treatment. In this study, we have presented a simple yet effective image processing approach for detecting whether the skin is affected by disease or not, using thresholding techniques implemented in MATLAB. The primary goal of this research is to develop an automated method for distinguishing between healthy and diseased skin based on visual characteristics in digital images. The methodology begins with the acquisition of high-resolution skin images, which are then preprocessed using standard techniques such as noise reduction and contrast enhancement to improve the visibility of affected regions. Following this, the images are converted to grayscale to reduce computational complexity while preserving critical textural features. The core of the detection process is based on thresholding, a segmentation technique that divides an image into foreground and background regions. Once the threshold is applied, a binary image is produced where white pixels represent potentially diseased regions and black pixels denote healthy skin. Post-thresholding, morphological operations are applied to refine the segmentation and eliminate noise. The extracted features are then analyzed to determine whether the skin is affected by disease, based on size, shape, and texture of the highlighted regions. The results demonstrate that thresholding, despite its simplicity, can be a powerful tool for preliminary skin disease detection. The use of MATLAB provides a flexible and efficient environment for rapid development and visualization of the image processing pipeline. This method can be a valuable component in telemedicine applications or as a supportive tool for dermatologists in early screening, especially in low-resource settings where access to advanced diagnostic tools is limited.