Application of Gabor wavelet and Locality Sensitive Discriminant Analysis for automated identification of breast cancer using digitized mammogram images

Raghavendra, U.; Acharya, U. Rajendra; Fujita, Hamido; Gudigar, Anjan; Tan, Jen Hong; Shreesha, C

doi:10.1016/j.asoc.2016.04.036

Cited by 82 publications

(27 citation statements)

References 55 publications

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“…Its filter bank is one of the most practical tools for extracting directional textural features [34]. In the case that a Gabor function is used as the mother wavelet, the Gabor wavelet (GW) can be contemplated as a wavelet transform [46]. For 2D signals, a bidimensional Gabor wavelet is defined as below [5], [18], [42]:…”

Section: ) Gabor Waveletmentioning

confidence: 99%

A Machine Learning-Based Approach for Counting Blister Cards Within Drug Packages

Bahaghighat¹,

Akbari²,

Xin

2019

IEEE Access

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Nowadays with the rapid development of technologies, machine vision has been used widely in various industries. The main applications of machine vision in industrial product lines are quality control (QC) and quality assurance (QA). The intelligent defects and anomalies recognition throughout the supply chain have come to be an integral part of quality control systems, in particular, in the food and pharmaceutical industries. In these industries, it is a legal requirement in manufacturing processes which can lead to minimizing the total number of defected products as well as maximizing the performance. In this paper, the machine vision has been utilized to monitor and control the proper packaging of drugs in pharmaceutical product lines. The main goal is counting the number of blister cards within a drug package. To tackle this problem, a new model based on object detection, feature extraction, and classification is proposed. Thanks to several strong approaches, such as the Haar cascade, HOG, ORG, Gabor wavelet, Radon transform, KNN, and SVM, and the accuracy over 88% is achieved in our experiments.

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Section: ) Gabor Waveletmentioning

confidence: 99%

A Machine Learning-Based Approach for Counting Blister Cards Within Drug Packages

Bahaghighat¹,

Akbari²,

Xin

2019

IEEE Access

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“…Usually, FP reduction algorithm is postprocessing step of a CAD system with two stages namely: Feature extraction and Classification. Various methods have been developed for feature extraction based on wavelets [ 8 , 18 , 32 ], curvelet [ 33 , 34 ], Gabor [ 35 , 36 ], morphological descriptors [ 20 ], textural analysis [ 26 , 27 , 30 , 32 ], histogram [ 4 , 5 , 7 , 29 , 37 – 40 ], etc. The segmentation error can reduce the performances of morphological descriptor.…”

Section: Literature Surveymentioning

confidence: 99%

Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms

Pawar¹,

Talbar

Dudhane

2018

Journal of Healthcare Engineering

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Breast Cancer is the most prevalent cancer among women across the globe. Automatic detection of breast cancer using Computer Aided Diagnosis (CAD) system suffers from false positives (FPs). Thus, reduction of FP is one of the challenging tasks to improve the performance of the diagnosis systems. In the present work, new FP reduction technique has been proposed for breast cancer diagnosis. It is based on appropriate integration of preprocessing, Self-organizing map (SOM) clustering, region of interest (ROI) extraction, and FP reduction. In preprocessing, contrast enhancement of mammograms has been achieved using Local Entropy Maximization algorithm. The unsupervised SOM clusters an image into number of segments to identify the cancerous region and extracts tumor regions (i.e., ROIs). However, it also detects some FPs which affects the efficiency of the algorithm. Therefore, to reduce the FPs, the output of the SOM is given to the FP reduction step which is aimed to classify the extracted ROIs into normal and abnormal class. FP reduction consists of feature mining from the ROIs using proposed local sparse curvelet coefficients followed by classification using artificial neural network (ANN). The performance of proposed algorithm has been validated using the local datasets as TMCH (Tata Memorial Cancer Hospital) and publicly available MIAS (Suckling et al., 1994) and DDSM (Heath et al., 2000) database. The proposed technique results in reduction of FPs from 0.85 to 0.02 FP/image for MIAS, 4.81 to 0.16 FP/image for DDSM, and 2.32 to 0.05 FP/image for TMCH reflecting huge improvement in classification of mammograms.

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“…The GW is a popular technique used filters for extracting image features [65] . It is calculated by Eq.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions

Yousri

Elaziz

Abualigah

et al. 2021

Applied Soft Computing

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Application of Gabor wavelet and Locality Sensitive Discriminant Analysis for automated identification of breast cancer using digitized mammogram images

Cited by 82 publications

References 55 publications

A Machine Learning-Based Approach for Counting Blister Cards Within Drug Packages

A Machine Learning-Based Approach for Counting Blister Cards Within Drug Packages

Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms

COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions

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