Advances in Medical Diagnostic Technology by Khin Wee Lai, Yan Chai Hum, Maheza Irna Mohamad Salim,

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By Khin Wee Lai, Yan Chai Hum, Maheza Irna Mohamad Salim, Sang-Bing Ong, Nugraha Priya Utama, Yin Mon Myint, Norliza Mohd Noor, Eko Supriyanto

This e-book presents the newest findings and data in complicated diagnostics expertise, overlaying a large spectrum together with mind job research, breast and lung melanoma detection, echocardiography, computing device aided skeletal evaluate to mitochondrial biology imaging on the mobile point. The authors explored magneto acoustic methods and tissue elasticity imaging for the aim of breast melanoma detection. views in fetal echocardiography from a picture processing perspective are integrated. Diagnostic imaging within the box of mitochondrial ailments in addition to using Computer-Aided procedure (CAD) also are mentioned within the ebook. This booklet can be valuable for college kids, teachers or expert researchers within the box of biomedical sciences and photograph processing.

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Extra resources for Advances in Medical Diagnostic Technology

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The objective of this thesis is to provide a solution to the mentioned unsolved technical problem in segmentation for automated BAA system. The task is accomplished by first applying the modified histogram equalized module, then undergoing the proposed automated anisotropic diffusion, following by a novel Y. C. my K. W. my N. P. Utama Á Y. M. my Y. M. my M. I. my K. W. 1007/978-981-4585-72-9_2, Ó Springer Science+Business Media Singapore 2014 23 24 Y. C. Hum et al. fuzzy quadruple division scheme to optimize the central segmentation algorithm, and finally, the process ends with an additional quality assurance scheme.

2008). One of the popular dynamic thresholding methods is Niback method (Niblack 1990). Generally, dynamic thresholding performs better than global thresholding and local thresholding. However, it has similar drawback as local thresholding that we need to determine the kernel size; the threshold has to be selected manually depending on application. Only suitable selection of kernel size and threshold can produce optimum result of segmentation. In addition, dynamic thresholding consumes much more computational resources relative to local thresholding and global thresholding due to its pixel-wise nature.

Unable to segment multiple objects. 3. High sensitivity to environmental noises in image. 4. High dependency toward intensity gradient along the edges. 5. Do not consider the region information of the targeted objects. 6. High dependency on initial guessed point location. If the initial snake is not sufficiently close to targeted object boundaries, then points in snake can hardly attach the boundaries. 7. Difficult to grow into concavity. 8. Do not have a global shape controller that constraints the shape of contour from deviating from allowable shape of the targeted object.

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