A novel breast tumor classification algorithm using neutrosophic score features

Amin, Khalid M.; Shahin, A.I.; Guo, Yanhui

doi:10.1016/j.measurement.2015.12.013

Cited by 31 publications

(22 citation statements)

References 25 publications

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“…NSS is defined by [20] to measure the similarity degree between different elements, and has been applied widely in image processing as mentioned before due to its ability to describe the indeterminate information such as noises and vague boundary in images. A neutrosophic set can be defined as [21]: let A = A 1 , A 2 , . .…”

Section: Related Workmentioning

confidence: 99%

A novel enhancement technique for pathological microscopic image using neutrosophic similarity score scaling

Shahin¹,

Amin

Sharawi

et al. 2018

Optik

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In 2011, Food and Drug Administration (FDA or USFDA) certified the automated cell morphology (ACM) systems for medical use in USA. The brightness, contrast and color appearance are all factors that play a major role in the diagnosis of many blood diseases. Accordingly, enhancement of pathological microscopic image (PMI) is a crucial step to increase the efficiency of computer assisted software. Some of the previous PMI enhancement methods neglected the illumination information and others used a reference image for template matching. These methods worked under strictly controlled conditions. In this paper, a robust technique is proposed for pathological images enhancement based on neutrosophic similarity score scaling. The color image is separated into three channels, and then each channel is represented in the neutrosophic domain into three subsets T, I and F. Neutrosophic similarity score (NSS) under multi-criteria are computed and used to scale the input image. The main contribution of this paper is that red, green and blue coefficients derived from the neutrosophic calculations lead directly to an adaptive pathology image enhancement and take into consideration many color image quality (IQ) parameters like illumination, contrast and color balance where it does not focus on a single IQ parameter like previous methods. In the experiments, several microscopic image quality measurements are utilized to evaluate the proposed method's performance versus the previous enhancement techniques. The experimental results demonstrate that our proposed system is promising with low complexity, adaptive with different resolution and lighting conditions. This provides the basis for automatic medical diagnosis and further processing of medical images.

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Section: Related Workmentioning

confidence: 99%

A novel enhancement technique for pathological microscopic image using neutrosophic similarity score scaling

Shahin¹,

Amin

Sharawi

et al. 2018

Optik

Self Cite

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“…Other than the texture features obtained conventionally from the smallest area covering the tumour (ROI), Cheng et al [10] have obtained texture-based features that also include a morphological feature (size) besides the texture information using together the number of lateral/depth pixels and grey-level values of ROI. Amin et al [28] divided the smallest rectangular area enclosing the tumour into 9 images and gathered the first-order texture features obtained from each sub-image into the sets called vertical, horizontal and central orientation. They also obtained a new texture-based feature set by dividing the texture feature values obtained from the whole ROI by the values obtained from these sets [28].…”

Section: Texture Features Extractionmentioning

confidence: 99%

“…Amin et al [28] divided the smallest rectangular area enclosing the tumour into 9 images and gathered the first-order texture features obtained from each sub-image into the sets called vertical, horizontal and central orientation. They also obtained a new texture-based feature set by dividing the texture feature values obtained from the whole ROI by the values obtained from these sets [28].…”

Section: Texture Features Extractionmentioning

confidence: 99%

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A novel classifier model for mass classification using BI-RADS category in ultrasound images based on Type-2 fuzzy inference system

Uzunhisarcikli

Göreke

2018

Sādhanā

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Ultrasound imaging is an imaging technique for early detection of breast cancer. Breast Imaging Reporting and Data System (BI-RADS) lexicon, developed by The American College of Radiology, provides a standard for expert doctors to interpret the ultrasound images of breast cancer. This standard describes the features to classify the tumour as benign or malignant and it also categorizes the biopsy requirement as a percentage. Biopsy is an invasive method that doctors use for diagnosis of breast cancer. Computer-aided detection (CAD)/diagnosis systems that are designed to include the feature standards used in benign/malignant classification help the doctors in diagnosis but they do not provide enough information about the BI-RADS category of the mass. These systems classify the benign tumours with 90% biopsy possibility (BI-RADS-4) and with 2% biopsy possibility (BI-RADS-2) in the same category. There are some studies in the literature that make category classification via commonly used classifier methods but their success rates are low. In this study, a twolayer, high-success-rate classifier model based on Type-2 fuzzy inference is developed, which classifies the tumour as benign or malignant with its BI-RADS category by incorporating the opinions of the expert doctors. A 99.34% success rate in benign/malignant classification and a 92% success rate in category classification (BI-RADS 2, 3, 4, 5) were obtained in the accuracy tests. These results indicate that the CAD system is valuable as a means of providing a second diagnostic opinion when radiologists carry out mass diagnosis.

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“…Since a neutrosophic set (NS) [1] provides an effective way to express inconsistent, incomplete, and indeterminate information in the real world, which cannot be expressed by the fuzzy set and (interval-valued) intuitionistic fuzzy set [2][3][4][5], it has been widely applied in various fields, such as image processing [6][7][8][9], object tracking [10][11][12], and decision-making [13]. As a subclass of NS, a simplified neutrosophic set (SNS) [14], implying single-valued neutrosophic set (SVNS) and interval neutrosophic set (INS) concepts, is composed of the truth, indeterminacy, and falsity components, where their membership degrees are constrained in the real standard interval [0,1].…”

Section: Introductionmentioning

confidence: 99%

Improved Symmetry Measures of Simplified Neutrosophic Sets and Their Decision-Making Method Based on a Sine Entropy Weight Model

Cui

2018

Symmetry

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This work indicates the insufficiency of existing symmetry measures (SMs) between asymmetry measures of simplified neutrosophic sets (SNSs) and proposes the improved normalized SMs of SNSs, including the improved SMs and weighted SMs in single-valued and interval neutrosophic settings. Then, the sine entropy measures of SNSs are presented to establish a sine entropy weight model for solving the criteria weights in decision-making. Based on the improved weighted SMs of SNSs and the sine entropy weight model, a multi-criteria decision-making (MCDM) method with unknown criteria weights (an improved MCDM method) is established in the SNS setting. In the MCDM process, corresponding to the criteria weights obtained by the sine entropy model, the ranking order of all alternatives and the best one are given by means of the improved weighted SMs between the ideal solution and each alternative. Lastly, the improved MCDM method is applied to an actual decision example in single-valued and interval neutrosophic settings to indicate the feasibility of the improved MCDM method. By comparative analysis with existing MCDM methods, the improved SMs and the sine entropy weight model not only provide a simpler and more effective method for MCDM problems with unknown criteria weights in the SNS setting, but can also overcome the insufficiency of the existing SMs and MCDM method.

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A novel breast tumor classification algorithm using neutrosophic score features

Cited by 31 publications

References 25 publications

A novel enhancement technique for pathological microscopic image using neutrosophic similarity score scaling

A novel enhancement technique for pathological microscopic image using neutrosophic similarity score scaling

A novel classifier model for mass classification using BI-RADS category in ultrasound images based on Type-2 fuzzy inference system

Improved Symmetry Measures of Simplified Neutrosophic Sets and Their Decision-Making Method Based on a Sine Entropy Weight Model

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