Abstract
Magnetic Resonance Imaging (MRI) is a critical tool in medical diagnostics, particularly for cancer detection and classification. However, the quality of MRI images often suffers from noise, low contrast, and intensity inhomogeneity, which can hinder accurate diagnosis. Image enhancement techniques, such as histogram processing, play a crucial role in improving image quality by enhancing contrast and emphasizing important fea- tures. This paper explores the application of histogram processing for MRI image enhancement, focusing on contrast enhancement and noise reduction techniques. The enhanced images are subsequently processed for feature extraction, which is crucial for the accurate classification of cancerous tissues. Key features such as texture, shape, and intensity are extracted to distinguish between malignant and be- nign tumors. The classification process utilizes machine learning algorithms, which are trained on the extracted features to achieve high accuracy in cancer classification. The proposed method demonstrates significant improvement in image quality, leading to better feature extraction and more accurate cancer diagnosis. This approach ultimately aids in early detection and improved treatment planning for patients. Index Terms—Image enhancement, Histogram processing, Seg- mentation, Feature extraction, SVM classifier.
Published Version
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