Medical imaging plays a crucial role in the diagnosis and treatment of various diseases, including brain tumors. Magnetic Resonance Imaging (MRI) is a sophisticated imaging technique that produces high-resolution images of the brain, but interpreting and analyzing the vast amount of data in these images is a challenging task. To address this challenge, image processing techniques are employed to extract relevant information from MRI images. In this context, tumor detection and segmentation are essential for diagnosing brain tumors accurately.
In recent years, brain tumor segmentation in MRI has become an emerging research area in medical imaging. This research aims to develop an efficient and automated brain tumor detection method that can classify tissues into normal, benign, and malignant categories. This detection method involves four stages: pre-processing of MR images, feature extraction, and classification.
This paper proposes a novel approach to detect and extract brain tumors from MRI images using a spatial fuzzy C-means clustering algorithm. The approach also incorporates noise removal, morphological tasks, and other medical imaging techniques to produce high-quality segmentations. The features of the image are then extracted using the Dual-Tree Complex wavelet transformation (DTCWT) method, and a Back Propagation Neural Network (BPN) is used to classify the normal and abnormal brain.
This proposed algorithm is highly efficient and can significantly reduce diagnosis time while also improving accuracy. Its potential applications include diagnostic and therapeutic applications, and it can aid in the early detection and treatment of brain tumors.
MATLAB R2016a or later.
C-means Clustering, Magnetic Resonance Imaging, Segmentation, Tumor Detection.