Feature selection in mammogram image using rough set approach

Keerthana, K.; Thangavel, K.

doi:10.1109/ncoiet.2011.5738820

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…The standard deviation vector σ is obtained using Equation 4: (4) where N and M are the numbers of rows and columns respectively, g c (i, j) is the centre pixel at position (i, j), g c (i, j) is the neighbourhood of g c (i, j) lying along the orientation 2pπ/P with the radius R and μ p is the oriented mean obtained using the following: ALBPS descriptor is obtained by concatenating the P + 2 bin histogram values of the uniform LBP approach together with the P-dimensional standard deviation vector, yielding a descriptor of 2P + 2 features with P as the size of the neighbourhood.…”

Section: Adaptive Local Binary Patternmentioning

confidence: 99%

“…The first works in texture classification focus on the statistical analysis of images. Some representative methods [2][3][4] rely on texture features determined from the grey level co-occurrence matrix (GLCM) by Haralick [5], while others are based on Gabor filter bank responses, one of the most prevailing filters [6]. Local binary patterns (LBP), proposed by Ojala et al [7], are a recent method commonly used to describe texture.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive local binary pattern with oriented standard deviation (ALBPS) for texture classification

et al. 2013

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A new method to describe texture images using a hybrid combination of local and global texture descriptors is proposed in this paper. In this regard, a new adaptive local binary pattern (ALBP) descriptor is presented in order to carry out the local description. It is built by adding oriented standard deviation information to an ALBP descriptor in order to achieve a more complete representation of the images, and hence, it has been called adaptive local binary pattern with oriented standard deviation (ALBPS). Regarding semen vitality assessment, ALBPS outperformed previous literature works with an 81.88% accuracy and also yielded higher hit rates than the LBP and ALBP baseline methods. Concerning the global description of the images, several classical texture algorithms were tested and a descriptor based on wavelet transform and Haralick feature extraction (wavelet concurrent feature 13 (WCF13)) obtained the best results. Both local and global descriptors were combined, and the classification was carried out with a support vector machine. Two data sets have been evaluated: textures under varying illumination, pose and scale (KTH-TIPS) 2a data set and a second spermatozoa boar data set used to distinguish between dead or alive sperm heads. Therefore, our proposal is novel in three ways. First, a new local feature extraction method ALBPS is introduced. Second, a hybrid method combining the proposed local ALBPS and a global descriptor is presented, outperforming our first approach and all other methods evaluated for this problem. Third, texture classification accuracy is greatly improved with the two former texture descriptors presented. F score and accuracy values were computed in order to measure the performance. The best overall result was yielded by combining ALBPS with WCF13, reaching an F score = 0.886 and an accuracy of 85.63% in the spermatozoa data set and an 84.45% of hit rate in the KTH-TIPS 2a.

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Section: Adaptive Local Binary Patternmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive local binary pattern with oriented standard deviation (ALBPS) for texture classification

et al. 2013

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“…Thus, it provides consistent performance, and it is not application specific. The proposed method is compared with Population-based optimization algorithms for feature selection Ant Colony Optimization (ACO) [11], and Quickreduct which is Rough set based feature selection [12,13]. An overview of the proposed system is offered below.…”

Section: Introductionmentioning

confidence: 99%

Quick Reduct-ACO based feature selection for TRUS prostate cancer image classification

Manavalan¹,

Thangavel

2012

International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)

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Ultrasound imaging is most suitable method for early detection of prostate cancer. It is very difficult to distinguish benign and malignant in the early stage of cancer. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based classification system can provide a second opinion to the radiologists. Generally objects are described in terms of a set of measurable features in pattern recognition. Feature selection is a process of selecting the most wanted or dominating features set from the original features set in order to reduce the cost of data visualization and increasing classification efficiency and accuracy. The Region of Interest (ROI) is identified from the Transrectal Ultrasound (TRUS) images using DBSCAN clustering with morphological operators. Then the statistical texture features are extracted from the ROIs. Rough Set based Quick Reduct (QR) and Evolutionary based Ant Colony Optimization (ACO) is studied.In this paper, Hybridization of Rough Set based QR and ACO is proposed for dimensionality reduction. The selected features may have the best discriminatory power for classifying prostate cancer based on TRUS images. Support Vector Machine (SVM) is tailored for evaluation of the proposed feature selection methods through classification. Then, the comparative analysis is performed among these methods. Experimental results show that the proposed method QR-ACO produces significant results. Number of features selected using QR-ACO algorithm is minimal, and is successful and has high detection accuracy.

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Feature selection in mammogram image using rough set approach

Cited by 2 publications

References 9 publications

Adaptive local binary pattern with oriented standard deviation (ALBPS) for texture classification

Adaptive local binary pattern with oriented standard deviation (ALBPS) for texture classification

Quick Reduct-ACO based feature selection for TRUS prostate cancer image classification

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