Brain magnetic resonance image (MRI) segmentation using multimodal optimization

Taymaz Akan

One of the highly focused areas in the medical science community is segmenting tumors from brain magnetic resonance imaging (MRI). The diagnosis of malignant tumors at an early stage is necessary to provide treatment for patients. The patient’s prognosis will improve if it is detected early. Medical experts use a manual method of segmentation when making a diagnosis of brain tumors. This study proposes a new approach to simplify and automate this process. In recent research, multi-level segmentation has been widely used in medical image analysis, and the effectiveness and precision of the segm ...Daha fazlası

Makale 2024 Multimedia Tools and Applications 2 Görüntülenme

Süresiz Ambargo

Multilevel image thresholding with multimodal optimization

Taymaz Akan

Thresholding method is one of the most popular approaches for image segmentation where an objective function is defined in terms of threshold numbers and their locations in a histogram. If only a single threshold is considered, a segmented image with two classes is achieved. On the other hand, multiple classes in the output image are created with multilevel thresholding. Otsu and Kapur's procedures have been conventional steps for defining objective functions. Nevertheless, the fundamental problem with thresholding techniques is the determination of threshold numbers, which must be selected by ...Daha fazlası

Makale 2021 Multimedia Tools and Applications80 ( 10 ) , pp. 1 - 15289 121 Görüntülenme Süresiz Ambargo

Erişime Açık

Battle royale optimizer for multilevel image thresholding

Taymaz Akan

Image segmentation, the process of partitioning an image into meaningful regions, is a fundamental step in image processing, crucial for applications like computer vision, medical imaging, and object recognition. Image segmentation is an essential step of image processing that directly affects its success. Among the methods used for image segmentation, histogram-based thresholding is prevalent. Two well-known approaches to histogram-based thresholding are Otsu’s and Kapur’s methods in gray images that maximize the between-class variance and the entropy measure, respectively. Both techniques we ...Daha fazlası

Makale 2024 Journal of Supercomputing80 ( 4 ) , pp. 5 - 5340 36 Görüntülenme