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 offers the newest findings and data in complex diagnostics know-how, overlaying a large spectrum together with mind task research, breast and lung melanoma detection, echocardiography, computing device aided skeletal review to mitochondrial biology imaging on the mobile point. The authors explored magneto acoustic ways and tissue elasticity imaging for the aim of breast melanoma detection. views in fetal echocardiography from a picture processing perspective are incorporated. Diagnostic imaging within the box of mitochondrial illnesses in addition to using Computer-Aided method (CAD) also are mentioned within the publication. This ebook can be worthy for college kids, academics or specialist researchers within the box of biomedical sciences and photograph processing.
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Extra info for Advances in Medical Diagnostic Technology
2011). The deformable models, ASM and AAM, undoubtedly are powerful segmentation methods. However, they are not without weaknesses and are not best method for automated hand segmentation. The reasons are summarized as following: 1. The landmarks placement has to be manually annotated by users. Incorrect landmarks placements lead to unreliable capture of shape variability. 2. The number of landmarks has to be specified by user manually. Insufficient landmarks lead to failure in obtaining the shape of the targeted structures; excessive landmarks lead to computational inefficiency.
2011). Conventionally, the left-hand radiograph is utilized to measure skeletal maturation because it is proven that the skeletal growth of hand represents the biological maturity. Such analysis of maturation is predicated on important growth features such as ossification area and calcium position in the ossification area (Roche and French 1970). Diseases such as endocrine disorders (Chemaitilly and Sklar 2010), chromosomal disorders, and early sexual maturation can be indicated through the calculated difference between the skeletal age and biological age (Heinrich 1986).
Hsieh et al. (2007) incorporate adaptive segmentation method with Gibbs random field at the preprocessing stage. Zhang et al. (2007) suggest segmenting the carpal by non-liner filter as preprocessing follows by adaptive image threshold setting, binary image labeling, and small object removal. However, it involves user-specified threshold and Canny edge detection that are not robust in segmentation. Similarly, Somkantha et al. (2011a, b) segment the carpals bone using a combination of vector image model and Canny edge detector.