Biomedical Image Processing / Medical Image Processing
Mohammad Mahdi Alimoradi; Mohammad Bagher Khodabakhshi; Shahriar Jamasb
Volume 17, Issue 1 , May 2023, , Pages 61-70
Abstract
Stroke is one of the causes of death and the main cause of disability in developed countries. Normally, identification of stroke lesions is done by magnetic imaging, and its analysis requires the continuous presence of a doctor in the treatment center. Therefore, intelligent processing of medical images ...
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Stroke is one of the causes of death and the main cause of disability in developed countries. Normally, identification of stroke lesions is done by magnetic imaging, and its analysis requires the continuous presence of a doctor in the treatment center. Therefore, intelligent processing of medical images will be an effective approach for automatic diagnosis of brain lesions.In this paper, a new integrated framework based on fuzzy inference system and deep neural network for automatic segmentation of brain lesions is introduced. In this regard, firstly, an improved U-net deep network (U-net) has been introduced for lesion detection and segmentation, which includes increasing the number of encoder and decoder layers along with changing the activation functions. Then, by using a fuzzy inference system based on if-then rules used by membership functions, the proposed approach of this study, which is based on the pre-processing of input images and the use of the unit network, has been introduced.The results showed that the integration of the fuzzy inference system in the pre-processing with the improved deep network could increase the DICE coefficient up to 0.84. In addition, improving the contrast of the input images by the fuzzy system compared to the usual pre-processing methods such as histogram equalization showed a much better performance in the detection of lesions with small dimensions, which is due to the ability to control the amount of contrast increase in the fuzzy systems compared to the usual methods.
Biomedical Image Processing / Medical Image Processing
Hamid Abrishami Moghaddam; Maryam Momeni; Kamran Kazemi; Reinhard Grebe; Fabrice Wallois
Volume 4, Issue 4 , June 2010, , Pages 337-360
Abstract
Diagnostic follow-up of the brain development during the neonatal period and childhood is an important clinical task. Any disturbance of this process can cause pathological deviations, especially if the baby is born premature. Recent advances in magnetic resonance imaging allow obtaining high-resolution ...
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Diagnostic follow-up of the brain development during the neonatal period and childhood is an important clinical task. Any disturbance of this process can cause pathological deviations, especially if the baby is born premature. Recent advances in magnetic resonance imaging allow obtaining high-resolution images of the neonatal brain. After segmenting the brains they can be used to reconstruct and model changes occurring during neonatal brain development. In addition such near-realistic model of the head, including the skin, skull and brain can be used to solve the inverse problem of determining the sources of registered signals from electrical brain activity. Although there exist numerous methods and various modeling schemes for adults, these cannot be used directly for neonates due to important differences in morphology. In this review article, neonatal brain atlases are divided into three categories: individual atlases, probabilistic atlases and stochastic atlases. In the following, existing neonatal brain atlases are placed in this classification and their methods of construction are presented. Furthermore, strengths and weaknesses of those neonatal brain atlases are analyzed and finally future research trends in this area are explained.
