An improved scheme for minimum cross entropy threshold selection based on genetic algorithm

Tang, Kezong; Yuan, Xiaojing; Sun, Tao; Yang, Jingyu; Shang, Gao

doi:10.1016/j.knosys.2011.02.013

Cited by 76 publications

(18 citation statements)

References 25 publications

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“…Colour is one of the most significant low-level feature that can be used to extract homogeneous regions which are related to objects or part of objects most of the time, multilevel thresholding technique approaches [50,51], thresholding approach in Otsu algorithm [52], threshold approach in segmentation of satellite images [53], and other applications [15,54]. …”

Section: Methodsmentioning

confidence: 99%

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Drulytė

Ruzgas

Raišutis

et al. 2018

Libyan Journal of Medicine

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Ultrasonic and digital dermatoscopy diagnostic methods are used in order to estimate the changes of structure, as well as to non-invasively measure the changes of parameters of lesions of human tissue. These days, it is very actual to perform the quantitative analysis of medical data, which allows to achieve the reliable early-stage diagnosis of lesions and help to save more lives. The proposed automatic statistical post-processing method based on integration of ultrasonic and digital dermatoscopy measurements is intended to estimate the parameters of malignant tumours, measure spatial dimensions (e.g. thickness) and shape, and perform faster diagnostics by increasing the accuracy of tumours differentiation. It leads to optimization of time-consuming analysis procedures of medical images and could be used as a reliable decision support tool in the field of dermatology.

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Section: Methodsmentioning

confidence: 99%

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Drulytė

Ruzgas

Raišutis

et al. 2018

Libyan Journal of Medicine

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“…Fourthly and fifthly, the maximum and minimum thresholding entropy is a thresholding algorithm based on the entropy distribution from the degree of gray image. The maximum entropy obtained based on the maximization of the entropy value of the two classes is foreground and background [15]. Meanwhile, the search process of threshold value in minimal entropy is based on the minimizing of entropy value between the two classes.…”

Section: Segmentation Bmentioning

confidence: 99%

A Comparative Study of Thresholding Algorithms on Breast Area and Fibroglandular Tissue

Uyun¹,

Hartati²,

Harjoko³

et al. 2015

ijacsa

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Abstract-One of the independently risk factors of breast cancer is mammographic density reflecting the composition of the fibroglandular tissue in breast area. Tumor in the mammogram is precisely complicated to detect as it is covered by the density (the masking effect). The determination of mammographic density may be implemented by calculating percentage of mammographic density (quantitative and objective approaches). Thereby, the use of a proper thresholding algorithm is highly required in order to obtain the fibroglandular tissue area and breast area. The mammograms used in the research were derived from Oncology Clinic, Yogyakarta that had been verified by Radiologists using semi-automatic thresholding. This research was aimed to compare the performance of the thresholding algorithm using three parameters, namely: PME, RAE and MHD. Zack Algorithm had the best performance to obtain the breast area with PME, RAE and MHD of about 0.33%, 0.71% and 0.01 respectively. Meanwhile, there were two algorithms having good performance to obtain the fibroglandular tissue area, i.e. multilevel thresholding and maximum entropy with the value for PME (13.34%; 11:27%), RAE (53.34%; 51.26%) and MHD (1:47; 33.92) respectively. The obtained results suggest that zack algorithm is perfectly suited for getting breast area than multilevel thresholding and maximum entropy for getting fibroglandular tissue. It is one of the components to determine risk factors of breast cancer based on percentage of breast density.

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“…Main advantage of these algorithms is the domain independent nature. Secondly, these algorithms have the capability to find optimal or near optimal solutions in a large search space [24]. Therefore, in this paper, an attempt is made to find the optimal subset of MCs features from all possible subsets of features using GA, PSO and BBO.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Genetic Algorithm, Particle Swarm Optimization and Biogeography-based Optimization for Feature Selection to Classify Clusters of Microcalcifications

Khehra

Pharwaha

2016

J. Inst. Eng. India Ser. B

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Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2 n . Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.

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An improved scheme for minimum cross entropy threshold selection based on genetic algorithm

Cited by 76 publications

References 25 publications

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

A Comparative Study of Thresholding Algorithms on Breast Area and Fibroglandular Tissue

Comparison of Genetic Algorithm, Particle Swarm Optimization and Biogeography-based Optimization for Feature Selection to Classify Clusters of Microcalcifications

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