A novel automatic suspicious mass regions identification using Havrda &amp; Charvat entropy and Otsu's N thresholding

Kurt, Burçin; Nabiyev, Vasif V.; Turhan, Kemal

doi:10.1016/j.cmpb.2014.02.014

Cited by 38 publications

(21 citation statements)

References 9 publications

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“…Segmentation results obtained on suspicious ROI(s) are compared with the 2 other existing mass segmentation techniques, ie, Ostu's thresholding and Shi's level set segmentation method (SLSM) . Segmentation results on randomly chosen 9 mammograms from the MIAS dataset, ie, Mdb010, Mdb021, Mdb081, Mdb111, Mdb134, Mdb184, Mdb202, Mdb206, and Mdb271 are summarized in Figure .…”

Section: Resultsmentioning

confidence: 99%

“…Accuracy of 87.3% and 91.4% are observed by applying structured SVM with polynomial and Gaussian kernel, respectively. Entropy and thresholding‐based mass segmentation is applied by Kurt et al Sharma and Khanna developed a CAD system to detect the malignant mass by using Zernike moments . Nascimento et al used wavelet‐based texture features and polynomial classifier for mass classification on 360 mammograms from DDSM dataset.…”

Section: Related Workmentioning

confidence: 99%

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Mesh‐free based variational level set evolution for breast region segmentation and abnormality detection using mammograms

Kashyap

Bajpai

Khanna

et al. 2017

Numer Methods Biomed Eng

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Automatic segmentation of abnormal region is a crucial task in computer-aided detection system using mammograms. In this work, an automatic abnormality detection algorithm using mammographic images is proposed. In the preprocessing step, partial differential equation-based variational level set method is used for breast region extraction. The evolution of the level set method is done by applying mesh-free-based radial basis function (RBF). The limitation of mesh-based approach is removed by using mesh-free-based RBF method. The evolution of variational level set function is also done by mesh-based finite difference method for Proficiency of the algorithm is quantified by using sensitivity, specificity, and accuracy. The highest sensitivity, specificity, and accuracy of 93.96%, 95.01%, and 94.48%, respectively, are obtained on MIAS dataset using DRLBP feature with RBF kernel function. Whereas, the highest 92.31% sensitivity, 98.45% specificity, and 96.21% accuracy are achieved on DDSM dataset using DRLBP feature with RBF kernel function.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Mesh‐free based variational level set evolution for breast region segmentation and abnormality detection using mammograms

Kashyap

Bajpai

Khanna

et al. 2017

Numer Methods Biomed Eng

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“…Intensity-based approaches are based on the fact that the intensity range of a pectoral muscle region should be higher than the range of breast parenchyma. These approaches directly utilize the pixel intensities [2][3][4][5][6][7], image histograms [8][9][10], and image gradients [11], or they are applied to image gradients [12]. Additionally, there are also some studies that segment pectoral muscles in wavelet domain instead of spatial domain [13][14][15].…”

Section: Related Workmentioning

confidence: 99%

A novel multistage system for the detection and removal of pectoral muscles in mammograms

Esener¹,

Ergin²,

Yüksel³

2018

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, a novel multistage scheme for pectoral muscle removal from mammography images is proposed, and the performance of this system is verified using the publicly available Mammographic Image Analysis Society digital mammogram database. This database is composed of mediolateral oblique mammography images including three different tissue types (fatty, fatty-glandular, and dense-glandular) with three health status types (normal, benign cancer, and malignant cancer). In the implementation of the proposed system, a mammography image is first preprocessed by performing noise reduction background removal followed by artifact suppression processes. Then a presegmentation procedure is applied using region growing and line fitting is executed. Finally, pixels including pectoral muscle regions are removed from mammography images with an accuracy of 94.40%, sensitivity of 89.62%, and specificity of 99.99% after some postprocessing operations. Although the mean false positive rate obtained by the proposed approach is higher than that of other studies in the literature, not only the lower mean false negative rate but also the enhancement in the quality of pectoral muscle segmentation for all 322 images in the database evidently show the success of the proposed approach.

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“…This criterion defines one or more values, called thresholds, that divide the image in objects of interest and background. This technique is used in many works (Rosin and Ioannidis, 2003;Sezgin and Sankur, 2004) and applications (Mizushima and Lu, 2013;Kurt et al, 2014;Wang et al, 2015). Typically, there are two groups of thresholding methods.…”

Section: Bioleaf -Foliar Analysis: a Novel Approach To Estimate Leaf mentioning

confidence: 99%

BioLeaf: A professional mobile application to measure foliar damage caused by insect herbivory

Machado

Oruê

Arruda

et al. 2016

Computers and Electronics in Agriculture

100

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Soybean is one of the ten greatest crops in the world, answering for billion-dollar businesses every year. This crop suffers from insect herbivory that costs millions from producers. Hence, constant monitoring of the crop foliar damage is necessary to guide the application of insecticides. However, current methods to measure foliar damage are expensive and dependent on laboratory facilities, in some cases, depending on complex devices. To cope with these shortcomings, we introduce an image processing methodology to measure the foliar damage in soybean leaves. We developed a non-destructive imaging method based on two techniques, Otsu segmentation and Bezier curves, to estimate the foliar loss in leaves with or without border damage. We instantiate our methodology in a mobile application named BioLeaf, which is freely distributed for smartphone users. We experimented with realworld leaves collected from a soybean crop in Brazil. Our results demonstrated that BioLeaf achieves foliar damage quantification with precision comparable to that of human specialists. With these results, our proposal might assist soybean producers, reducing the time to measure foliar damage, reducing analytical costs, and defining a commodity application that is applicable not only to soy, but also to different crops such as cotton, bean, potato, coffee, and vegetables.

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A novel automatic suspicious mass regions identification using Havrda & Charvat entropy and Otsu's N thresholding

Cited by 38 publications

References 9 publications

Mesh‐free based variational level set evolution for breast region segmentation and abnormality detection using mammograms

Mesh‐free based variational level set evolution for breast region segmentation and abnormality detection using mammograms

A novel multistage system for the detection and removal of pectoral muscles in mammograms

BioLeaf: A professional mobile application to measure foliar damage caused by insect herbivory

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