PCA-PNN and PCA-SVM Based CAD Systems for Breast Density Classification

Kriti, .; Virmani, Jitendra; Dey, Nilanjan; Kumar, Vinod

doi:10.1007/978-3-319-21212-8_7

Cited by 58 publications

(19 citation statements)

References 49 publications

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“…Medical image enhancement techniques are used to improve some useful information in an image for physicians' accurate diagnoses and removal/reduction of some unwanted information. Recently, numerous studies are performed in the medical imaging domain as in [51]- [54].…”

Section: Resultsmentioning

confidence: 99%

Computed Tomography Image Enhancement Using Cuckoo Search: A Log Transform Based Approach

Ashour¹,

Samanta²,

Dey³

et al. 2015

JSIP

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Medical image enhancement is an essential process for superior disease diagnosis as well as for detection of pathological lesion accurately. Computed Tomography (CT) is considered a vital medical imaging modality to evaluate numerous diseases such as tumors and vascular lesions. However, speckle noise corrupts the CT images and makes the clinical data analysis ambiguous. Therefore, for accurate diagnosis, medical image enhancement is a must for noise removal and sharp/clear images. In this work, a medical image enhancement algorithm has been proposed using log transform in an optimization framework. In order to achieve optimization, a well-known meta-heuristic algorithm, namely: Cuckoo search (CS) algorithm is used to determine the optimal parameter settings for log transform. The performance of the proposed technique is studied on a low contrast CT image dataset. Besides this, the results clearly show that the CS based approach has superior convergence and fitness values compared to PSO as the CS converge faster that proves the efficacy of the CS based technique. Finally, Image Quality Analysis (IQA) justifies the robustness of the proposed enhancement technique.

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Section: Resultsmentioning

confidence: 99%

Computed Tomography Image Enhancement Using Cuckoo Search: A Log Transform Based Approach

Ashour¹,

Samanta²,

Dey³

et al. 2015

JSIP

Self Cite

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“…If the data set matrix X has a size n × d, then, when calculating the covariance matrix s t of the data set, it is equivalent to calculating a square matrix of d × d. If the dimension d is of a large order of magnitude, the operation time complexity will become very high, and a large amount of time is needed to calculate all the eigenvalues of the covariance matrix. In order to improve the time efficiency of the algorithm, a fast PCA algorithm was introduced in the experiment [12].…”

Section: Pca Fast Algorithmmentioning

confidence: 99%

Digital image technology based on PCA and SVM for detection and recognition of foreign bodies in lyophilized powder

Zhang

Luo

Deng

et al. 2020

THC

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BACKGROUND: Digital image technology has made great progress in the field of foreign body detection and classification, which is of great help to drug purity extraction and impurity analysis and classification. OBJECTIVE: The detection and classification of foreign bodies in lyophilized powder are important. The method which can obtain a higher accuracy of recognition needs to be proposed. METHODS: We used digital image technology to detect and classify foreign bodies in lyophilized powder, and studied the process of image preprocessing, median filtering, Wiener filtering and average filtering balance to better detect and classify foreign bodies in lyophilized powder. RESULTS: Through industrial small sample data simulation, test results show that in the process of image preprocessing, 3 3 median filtering is best. In the aspect of foreign body recognition, the recognition based on principal component analysis (PCA) and support vector machine (SVM) algorithm and the recognition based on PCA and Third-Nearest Neighbor classification algorithm are compared and results show that the PCA SVM algorithm is better. CONCLUSION: We demonstrated that integrating PCA and SVM to classify foreign bodies in lyophilized powder. Our proposed method is effective for the prediction of essential proteins.

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“…1. For the present work support vector machine [20][21][22] (SVM) is used as classifier by importing LibSVM 20 library files. For the evaluation of classification performance overall classification accuracy (OCA) is calculated, which is ratio of the number of correctly classified ROIs over the total number of actual ROIs.…”

Section: Classification Modulementioning

confidence: 99%

Wavelet Packet Texture Descriptors Based Four-class BIRADS Breast Tissue Density Classification

Kumar¹,

Bhadauria²,

Virmani

2015

Procedia Computer Science

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The present work proposes a computer aided diagnostic (CAD) system for four-class BIRADS breast tissue density classification. The study has been carried out on a total of 480 mammograms taken from the DDSM database. The ROI has been extracted manually from each image. Each ROI has been decomposed up to 2 nd level of decomposition using wavelet packet transform, resulting in 16 sub-band images for each ROI image. From each sub-band image three multi-resolution texture descriptors (i.e., mean, standard deviation and energy descriptors) are computed , resulting in texture feature vector of length 48 (16 X 3) for each ROI. The results obtained from the present work, indicate that the standard deviation and energy descriptors computed from subband images obtained by using wavelet packet transform with haar wavelet filter yield the highest overall classification accuracy of 73.7 % for four-class breast tissue density classification. Thus it can be concluded that standard deviation and energy multiresolution texture descriptors computed from sub-band images yielded by wavelet packet transform using haar wavelet filter contain significant information for differential diagnosis between four BIRADS breast tissue density classes.

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PCA-PNN and PCA-SVM Based CAD Systems for Breast Density Classification

Cited by 58 publications

References 49 publications

Computed Tomography Image Enhancement Using Cuckoo Search: A Log Transform Based Approach

Computed Tomography Image Enhancement Using Cuckoo Search: A Log Transform Based Approach

Digital image technology based on PCA and SVM for detection and recognition of foreign bodies in lyophilized powder

Wavelet Packet Texture Descriptors Based Four-class BIRADS Breast Tissue Density Classification

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