Haralick feature selection for material rigidity recognition using ultrasound echo

Object classification based on its rigidity requires principally the recognition of its material consistency. Generally, material consistency can be divided into two families, hard material and soft one. In this context, a new approach based on ultrasonic signal for consistency recognition of object materials is proposed. This approach allows distinguishing between the hard and the soft objects. Material consistency determination is based on Haralick's texture features. Then, a feature selection step is considered to select the most discriminative features. Only three Haralick features were used to assess the efficiency classifications models. As there is no affording dataset of ultrasonic signals acquired for material rigidity recognition, we develop our dataset using two ultrasonic sensors. In this context, no previous work has considered such a challenge. The analysis results show that three parameters (entropy, sum of entropy and variance) were found to be effective to discriminate between the two classes of material rigidity. The obtained results show the efficiency of the proposed method.

[1]  Dorra Sellami Masmoudi,et al.  New electronic cane for visually impaired people for obstacle detection and recognition , 2012, 2012 IEEE International Conference on Vehicular Electronics and Safety (ICVES 2012).

[2]  Ahmed Bouridane,et al.  Extraction of Haralick Features from Segmented Texture Multispectral Bio-Images for Detection of Colon Cancer Cells , 2011, 2011 First International Conference on Informatics and Computational Intelligence.

[3]  Michael Spann,et al.  Texture feature performance for image segmentation , 1990, Pattern Recognit..

[4]  Rupsa Saha,et al.  Using Haralick Features for the Distance Measure Classification of Digital Mammograms , 2014 .

[5]  Lindsay I. Smith,et al.  A tutorial on Principal Components Analysis , 2002 .

[6]  Jun'ichi Tsujii,et al.  Evaluation and Extension of Maximum Entropy Models with Inequality Constraints , 2003, EMNLP.

[7]  Dorra Sellami Masmoudi,et al.  New electronic white cane for stair case detection and recognition using ultrasonic sensor , 2013 .

[8]  David B. Knoester,et al.  Breast Cancer Detection Using Haralick Features of Images Reconstructed from Ultra Wideband Microwave Scans , 2014, CLIP@MICCAI.

[9]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[10]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[11]  Françoise Peyrin,et al.  Transformation de wigner-ville: description d’un nouvel outil de traitement du signal et des images , 1987 .

[12]  Nourhan Zayed,et al.  Statistical Analysis of Haralick Texture Features to Discriminate Lung Abnormalities , 2015, Int. J. Biomed. Imaging.

[13]  Weisheng Wang,et al.  A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm , 2017, Sensors.