A SVM Based Automated Detection of Uterine Fibroids Using Gabor and Wavelet Features

2012 ◽  
Vol 1 (1) ◽  
pp. 77-85
Author(s):  
N. Sriraam ◽  
D. Nithyashri ◽  
L. Vinodashri ◽  
P. Manoj Niranjan
Keyword(s):  
Support Vector Machines ◽  
Clinical Diagnosis ◽  
Classification Accuracy ◽  
Wavelet Packet ◽  
Uterine Fibroids ◽  
Gabor Filters ◽  
Automated Detection ◽  
Support Vector ◽  
Vector Machines ◽  
Wavelet Features

This paper suggests an automated detection procedure for identification of ultrasonic imaging based uterine fibroids using Gabor filters and wavelet features with support vector machines as classifier. A classification accuracy of 100% is achieved for 86 test images using wavelet packet features, which indicates the potential suitability of the proposed scheme for clinical diagnosis.

Classification of High Resolution Sar Imagery Using Local Indicators of Spatial Association

2013 ◽  
Vol 333-335 ◽  
pp. 1080-1084
Author(s):  
Zhang Fei ◽  
Ye Xi
Keyword(s):  
Support Vector Machines ◽  
High Resolution ◽  
Classification Accuracy ◽  
Spatial Association ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Good Classification ◽  
Sar Imagery

In this paper, we will propose a novel classification method of high-resolution SAR using local autocorrelation and Support Vector Machines (SVM) classifier. The commonly applied spatial autocorrelation indexes, called Moran's Index; Geary's Index, Getis's Index, will be used to depict the feature of the land-cover. Then, the SVM based on these indexes will be applied as the high-resolution SAR classifier. A Cosmo-SkyMed scene in ChengDu city, China is used for our experiment. It is shown that the method proposed can lead to good classification accuracy.

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