Medical images are obtained with computer‐aided diagnosis using electronic devices such as CT scanners and MRI machines. The captured computed tomography (CT)/magnetic resonance imaging (MRI) images typically have limited spatial resolution, low contrast, noise and nonuniform variability in intensity due to environmental effects. Therefore, the distinctions of the objects are blurred, distorted and the meanings of the objects are not quite precise. Fuzzy sets and fuzzy logic are best suited for addressing vagueness and ambiguity. Fuzzy clustering technique has been commonly used for segmentation of images throughout the last decade. This study presents a comparative study of 14 fuzzy‐clustered image segmentation algorithms used in the CT scan and MRI brain image segments. This study used 17 data sets including 4 synthetic data sets, namely, Bensaid, Diamond, Square, and its noisy version, 5 real‐world digital images, and 8 CT scan/MRI brain images to analyze the algorithms. Ground truth images are used for qualitative analysis. Apart from the qualitative analysis, the study also quantitatively evaluated the methods using three validity metrics, namely, partition coefficient, partition entropy, and Fukuyama‐Sugeno. After a thorough and careful review of the results, it is observed that extension of the fuzzy C‐means (EFCM) outperformed every other image segmentation algorithm, even in a noisy environment, followed by kernel‐based FCM σ, the output of which is also very good after EFCM.
This study applies concepts about computerization movements (CMs) to a case study of the diffusion of innovation in the developing world and thereby to draw lessons for undertaking similar technology projects.We identify the key characteristics of a computerization movement in the scholarly literature and then review the One Laptop Per Child (OLPC) Project in terms of each, identifying where OLPC adds new understanding about CMs. The OLPC project is an example of a computerization movement that has launched a new generation of low-cost computers in the developing world, while failing in its own ambitious goals. The OLPC project provides insights into the nature of computerization movements, in particular the process of mobilization, the diffusion of innovations in the developing world, and the overlap of multiple movements. OLPC's limited success to date illustrates the importance of having: (1) financial resources beyond deployment for economic sustainability, (2) local skills, infrastructure and deployment capability for operational sustainability, and (3) a replicable and scalable deployment model for ease of implementation across many sites.
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