Performance Evaluation of Feature Extraction methods for Classifying Abnormalities in Ultrasound Liver Images using Neural Network

Image analysis techniques have played an important role in several medical applications. In general, the applications involve the automatic extraction of features from the image which is further used for a variety of classification tasks, such as distinguishing normal tissue from abnormal tissue. In this paper, the classification of ultrasonic liver images is studied by using texture features extracted from Laws' method, autocorrelation method, Gabor wavelet and edge frequency method. The features from these methods are used to classify three sets of ultrasonic liver images-normal, cyst and benign and how well they suit in classifying the abnormalities is reported. A neural network classifier is employed to evaluate the performance of these features based on their recognition ability

[1]  A. Ahmadian,et al.  A method for texture classification of ultrasonic liver images based on Gabor Wavelet , 2004, Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004..

[2]  K. Laws.  Textured Image Segmentation , 1980 .

[3]  T.R. Lim,et al.  Car recognition using Gabor filter feature extraction , 2002, Asia-Pacific Conference on Circuits and Systems.

[4]  Takeo Ishigaki,et al.  Classification of breast tumors in mammograms using a neural network: utilization of selected features , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[5]  Kenneth I. Laws,et al.  Rapid Texture Identification , 1980, Optics & Photonics.

[6]  R.M. Haralick,et al.  Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.

[7]  S. Singh,et al.  Evaluation of texture methods for image analysis , 2001, The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001.

[8]  Chin-Tu Chen,et al.  An efficient algorithm to compute the complete set of discrete Gabor coefficients , 1994, IEEE Trans. Image Process..

[9]  Sameer Singh,et al.  Natural object classification using artificial neural networks , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[10]  Anil K. Jain,et al.  Texture Analysis , 2018, Handbook of Image Processing and Computer Vision.

[11]  Sameer Singh,et al.  Texture Analysis Experiments with Meastex and Vistex Benchmarks , 2001, ICAPR.

[12]  Sotiris Pavlopoulos,et al.  Computer assisted characterization of liver tissue using image texture analysis techniques on B-scan images , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[13]  Alireza Ahmadian,et al.  An efficient texture classification algorithm using Gabor wavelet , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[14]  Robert King,et al.  Textural features corresponding to textural properties , 1989, IEEE Trans. Syst. Man Cybern..

[15]  Yung-Chang Chen,et al.  Texture features for classification of ultrasonic liver images , 1992, IEEE Trans. Medical Imaging.