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Computer-Aided Diagnosis with Retinal Fundus Images
Published in de Azevedo-Marques Paulo Mazzoncini, Mencattini Arianna, Salmeri Marcello, Rangayyan Rangaraj M., Medical Image Analysis and Informatics: Computer-Aided Diagnosis and Therapy, 2018
In this chapter, image analysis methods for computer-aided diagnosis (CAD) based on retinal fundus images are described. The retina is the only area that allows direct and noninvasive observation of its blood vessels. Funduscopy is effective in the diagnosis of several diseases, such as glaucoma, diabetic retinopathy, and age-related macular degeneration (AMD). Moreover, by funduscopy, physicians may diagnose not only ophthalmological diseases but also systemic hypertension. The retina is located near the brain; thus, some conditions of the cerebrovascular system may be diagnosed by funduscopy [1]. There is a possibility that retinal arteriosclerosis may be a sign of cerebral hemorrhage. Thus, a patient may have a cerebral hemorrhage, a subarachnoid hemorrhage, or a hypertensive encephalopathy if they have a retinal hemorrhage. Since funduscopy is very useful in ophthalmology, neurosurgery, and cardiology, the retina is assessed in several medical departments. Therefore, a retinal CAD system can be useful for physicians in several medical departments. Arteries, veins, and other retinal anatomy are visualized in a retinal image as shown in Figure 2.1. Retinal condition is described by Keith-Wagner-Barker classification or Scheie classification. Detailed Scheie classification is shown in Table 2.1. It is divided into two diseases, hypertension and arteriosclerosis. The signs of low-grade diseases are related to blood vessels; thus, blood vessel segmentation techniques are very important and are described in Section 2.2. Section 2.3 introduces artery diameter measurement as a parameter to assist diagnosis of hypertensive retinopathy. Diabetic retinopathy and glaucoma are leading causes of blindness. CAD techniques for diabetic retinopathy and detection of hemorrhages and microaneurysms are described in Section 2 .4. In Section 2.5, techniques for detection of large cupping in the optic disc and nerve fiber layer defects are described for CAD of glaucoma.
Medical image fusion based on multi-scale decomposition using hybrid deep learning network model
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023
Syed Munawwar, P. V. Gopi Krishna Rao
Metastatic bronchogenic carcinoma and Hypertensive encephalopathy clinical cases images are performed here using CT and MRI, MRI, and SPECT fusions. Mild and Astrocytoma Alzheimer’s disease undergo CT and PET fusions to evaluate the fusion’s performance. 256 × 256 pixels is the resolution of the input images. The https://www.med.harvard.edu/aanlib/home.html website is used for collecting the dataset images. The experimental results of the first dataset (MRI-SPECT), second dataset (CT-PET), and third dataset (CT-MRI) are shown in Figures 3, 4 and 5.
Multimodal medical image fusion using residual network 50 in non subsampled contourlet transform
Published in The Imaging Science Journal, 2023
K. Koteswara Rao, K. Veera Swamy
To certify the presented algorithm, some experiments have been made taking different data sets of different persons suffering from (i) mild Alzheimer's, (ii) glioma, and (iii) hypertensive encephalopathy, etc. The offered one is tested with different baseline fusion techniques. Both subjective and objective analyzes have been done to assess the achievements.