Multi-scale feature identification using evolution strategies

Yuan, Xiaojing; Zhang, Jian; Yuan, Xiaohui; Buckles, Bill P.

doi:10.1016/j.imavis.2004.07.002

Cited by 14 publications

(5 citation statements)

References 20 publications

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“…Various researchers used principal component analysis (PCA) of a binary mask of the lesion, wavelet packet transform (WPT) [ 90 – 94 ], grey level cooccurrence matrix (GLCM) [ 61 , 95 ], Fourier power spectrum [ 96 ], Gaussian derivative kernels [ 97 ], and decision boundary feature extraction [ 98 – 100 ] in order to reduce data redundancy. Some of the typically used filter banks are Laws masks, the dyadic Gabor filter bank, and wavelet transform [ 101 ]. A particular problem in the related literature is that a significant number of studies do not report the details of their feature extraction procedure; see Table 6 .…”

Section: Computer-aided Diagnosis Systemmentioning

confidence: 99%

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Masood

Al-Jumaily

2013

International Journal of Biomedical Imaging

279

161

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Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided.

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Section: Computer-aided Diagnosis Systemmentioning

confidence: 99%

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Masood

Al-Jumaily

2013

International Journal of Biomedical Imaging

279

161

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“…Note that we decouple the data analysis from the data acquisition system to take advantage of the existing expertise of the imaging and video analysis within the group [6][7][8] and enable more flexibility to analyze cardiac optical imaging data from other data acquisition systems by adding file interfacing module to the COI-Wiz. The focus of the paper is the detailed implementation of the various video analytic algorithms in COI-Wiz.…”

Section: Framework Of Coi-wizmentioning

confidence: 99%

Coi-wiz: An interactive computer wizard for analyzing cardiac optical signals

Yuan

Uyanik

Situ

et al. 2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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A number of revolutionary techniques have been developed for cardiac electrophysiology research to better study the various arrhythmia mechanisms that can enhance ablating strategies for cardiac arrhythmias. Once the three-dimensional high resolution cardiac optical imaging data is acquired, it is time consuming to manually go through them and try to identify the patterns associated with various arrhythmia symptoms. In this paper, we present an interactive computer wizard that helps cardiac electrophysiology researchers to visualize and analyze the high resolution cardiac optical imaging data. The wizard provides a file interface that accommodates different file formats. A series of analysis algorithms output waveforms, activation and action potential maps after spatial and temporal filtering, velocity field and heterogeneity measure. The interactive GUI allows the researcher to identify the region of interest in both the spatial and temporal domain, thus enabling them to study different heart chamber at their choice.

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“…Following our previous work on region-based object identification [20][21][22], we developed an active contour-based region-fusion framework. In this approach, a lesion is first segmented into small regions by imposing strict constraints on homogeneity and strong edges on the region-based snakes.…”

Section: Introductionmentioning

confidence: 99%