scholarly journals Automatic Detection of Myocardial Infarction Through a Global Shape Feature Based on Local Statistical Modeling

2016 ◽  
pp. 208-216 ◽  
Author(s):  
Mahdi Tabassian ◽  
Martino Alessandrini ◽  
Peter Claes ◽  
Luca De Marchi ◽  
Dirk Vandermeulen ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Statistical Modeling ◽  
Automatic Detection ◽  
Shape Feature ◽  
Global Shape ◽  
Feature Based

Optimization of Image Zernike Moments Shape Feature Based on Evolutionary Computation

2013 ◽  
pp. 199-216
Author(s):  
Maofu Liu ◽  
Huijun Hu
Keyword(s):  
Image Analysis ◽  
Evolutionary Computation ◽  
High Dimension ◽  
Optimization Algorithm ◽  
Feature Vector ◽  
Zernike Moments ◽  
Shape Feature ◽  
Original Image ◽  
Feature Based ◽  
Image Shape

The image shape feature can be described by the image Zernike moments. In this chapter, the authors point out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. Therefore, the optimization algorithm based on evolutionary computation is designed and implemented in this chapter to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm.

A216 Automatic Construction Method for Image and Shape Feature based Landmark

2015 ◽  
Vol 2015.14 (0) ◽  
pp. 273-276
Author(s):  
Daichi Abe ◽  
Satoshi Suzuki ◽  
Kouzirou Iizuka ◽  
Takashi Kawamura
Keyword(s):  
Construction Method ◽  
Shape Feature ◽  
Automatic Construction ◽  
Feature Based

HYBRID REGISTRATION OF CORRESPONDING MAMMOGRAM IMAGES FOR AUTOMATIC DETECTION OF BREAST CANCER

2007 ◽  
Vol 19 (06) ◽  
pp. 359-374 ◽  
Author(s):  
Yih-Chih Chiou ◽  
Chern-Sheng Lin ◽  
Cheng-Yu Lin
Keyword(s):  
Breast Cancer ◽  
Image Registration ◽  
Early Detection ◽  
Feature Matching ◽  
Automatic Detection ◽  
Spatial Transformation ◽  
Registration Method ◽  
Feature Based ◽  
Mammogram Images ◽  
Hybrid Registration

Mammogram registration is a critical step in automatic detection of breast cancer. Much research has been devoted to registering mammograms using either feature-matching or similarity measure. However, a few studies have been done on combining these two methods. In this research, a hybrid mammogram registration method for the early detection of breast cancer is developed by combining feature-based and intensity-based image registration techniques. Besides, internal and external features were used simultaneously during the registration to obtain a global spatial transformation. The experimental results indicates that the similarity between the two mammograms increases significantly after a proper registration using the proposed TPS-registration procedures.

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