1
|
Das P, Das A. Shift invariant extrema based feature analysis scheme to discriminate the spiculation nature of mammograms. ISA TRANSACTIONS 2020; 103:156-165. [PMID: 32216985 DOI: 10.1016/j.isatra.2020.03.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 03/11/2020] [Accepted: 03/12/2020] [Indexed: 06/10/2023]
Abstract
Since uncontrolled growth of malignant masses introduces uneven shape irregularities and spiculations in the boundary, shape representing shift invariant features are essential to resolve the problem of discrimination. However, ambiguous nature of shape, size, margin, orientation of masses produces imprecise feature values. In this view, a new concept of extrema based feature characterization scheme is proposed for capturing radiating nature of mass morphology. Computation of extrema patterns needs only few algorithmic steps. Beside this, present study employs an automated enhancement procedure to improve the classification accuracy. Experimental results show that extrema characterization reduces the feature redundancy to produce high efficiency in reasonably low time.
Collapse
Affiliation(s)
- Poulomi Das
- OmDayal Group of Institutions, Maulana Abul Kalam Azad University of Technology, India.
| | - Arpita Das
- Department of Radio Physics and Electronics, University of Calcutta, Rajabazar Science College Campus, India.
| |
Collapse
|
2
|
|
3
|
Deng H, Deng W, Sun X, Liu M, Ye C, Zhou X. Mammogram Enhancement Using Intuitionistic Fuzzy Sets. IEEE Trans Biomed Eng 2016; 64:1803-1814. [PMID: 27831857 DOI: 10.1109/tbme.2016.2624306] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Conventional mammogram enhancement methods use transform-domain filtering, which possibly produce some artifacts or not well highlight all local details in images. This paper presents a new enhancement method based on intuitionistic fuzzy sets. METHODS The presented algorithm initially separates a mammogram via a global threshold and then fuzzifies the image utilizing the intuitionistic fuzzy membership function that adopts restricted equivalence functions. After that, the presented scheme hyperbolizes membership degrees of foreground and background areas, defuzzifies the fuzzy plane, and achieves a filtered image via normalization. Finally, an enhanced mammogram is obtained by fusing the original image with filtered one. These implementations can be processed in parallel. RESULTS This algorithm can improve the contrast and visual quality of regions of interest. CONCLUSION Real data experiments demonstrate that our method has better performance regarding the improvement of contrast and visual quality of abnormalities in mammograms (such as masses and/or microcalcifications), compared with classical baseline methods. SIGNIFICANCE This algorithm has potential for understanding and determining abnormalities.
Collapse
|
4
|
Hanmandlu M, Arora S, Gupta G, Singh L. Underexposed and overexposed colour image enhancement using information set theory. THE IMAGING SCIENCE JOURNAL 2016. [DOI: 10.1080/13682199.2016.1215063] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
5
|
|
6
|
Kanelovitch L, Itzchak Y, Rundstein A, Sklair M, Spitzer H. Biologically Derived Companding Algorithm for High Dynamic Range Mammography Images. IEEE Trans Biomed Eng 2013; 60:2253-61. [DOI: 10.1109/tbme.2013.2252464] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
7
|
Jose J, Prahladan A, Nair MS. Speckle reduction and contrast enhancement of ultrasound images using anisotropic diffusion with Jensen Shannon divergence operator. Biomed Eng Lett 2013. [DOI: 10.1007/s13534-013-0091-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
|
8
|
Khehra BS, Pharwaha APS. DIGITAL MAMMOGRAM ENHANCEMENT USING KAPUR MEASURE OF ENTROPY AND MATHEMATICAL MORPHOLOGY. BIOMEDICAL ENGINEERING: APPLICATIONS, BASIS AND COMMUNICATIONS 2013. [DOI: 10.4015/s1016237213500294] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Mammography is the most reliable, effective, low cost and highly sensitive method for early detection of breast cancer. Mammogram analysis usually refers to the processing of mammograms with the goal of finding abnormality presented in the mammogram. Mammogram enhancement is one of the most critical tasks in automatic mammogram image analysis. Main purpose of mammogram enhancement is to enhance the contrast of details and subtle features while suppressing the background heavily. In this paper, a hybrid approach is proposed to enhance the contrast of microcalcifications while suppressing the background heavily, using fuzzy logic and mathematical morphology. First, mammogram is fuzzified using Gaussian fuzzy membership function whose bandwidth is computed using Kapur measure of entropy. After this, mathematical morphology is applied on fuzzified mammogram. Mathematical morphology provides tools for the extraction of microcalcifications even if the microcalcifications are located on a nonuniform background. Main advantage of Kapur measure of entropy over Shannon entropy is that Kapur measure of entropy has α and β parameters that can be used as adjustable values. These parameters can play an important role as tuning parameters in the image processing chain for the same class of images. Experiments have been conducted on images of mini-Mammogram Image Analysis Society (MIAS) database (UK). Experiment results of the proposed approach are compared with histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE) and fuzzy histogram hyperbolization (FHH) which are well-established image enhancement techniques. In order to validate the results, several different kinds of standard test images (fatty, fatty-glandular and dense-glandular) of mini-MIAS database are considered. Objective image quality assessment parameters: Target-to-background contrast enhancement measurement based on standard deviation (TBCSD), target-to-background contrast enhancement measurement based on entropy (TBCE), contrast improvement index (CII), peak signal-to-noise ratio (PSNR) and average signal-to-noise ratio (ASNR) are used to evaluate the performance of proposed approach. The experimental results show that the proposed approach performs well. This study can be a part of developing a computer-aided diagnosis (CAD) system for early detection of breast cancer.
Collapse
Affiliation(s)
- Baljit Singh Khehra
- Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, Punjab, India
| | - Amar Partap Singh Pharwaha
- Department of Electronics and Communications Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, Sangrur, Punjab, India
| |
Collapse
|
9
|
Vadivel A, Surendiran B. A fuzzy rule-based approach for characterization of mammogram masses into BI-RADS shape categories. Comput Biol Med 2013; 43:259-67. [DOI: 10.1016/j.compbiomed.2013.01.004] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2011] [Revised: 04/25/2012] [Accepted: 01/10/2013] [Indexed: 11/16/2022]
|
10
|
ADEL MOULOUD, ZUWALA DANIEL, RASIGNI MONIQUE, BOURENNANE SALAH. ENHANCEMENT OF MAMMOGRAPHIC PHANTOM FEATURES BY NOISE REDUCTION. INT J PATTERN RECOGN 2011. [DOI: 10.1142/s0218001407005788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A noise reduction scheme on digitized mammographic phantom images is presented. This algorithm is based on a direct contrast modification method with an optimal function, obtained by using the mean squared error as a criterion. Computer simulated images containing objects similar to those observed in the phantom are built to evaluate the performance of the algorithm. Noise reduction results obtained on both simulated and real phantom images show that the developed method may be considered as a good preprocessing step from the point of view of automating phantom film evaluation by means of image processing.
Collapse
Affiliation(s)
- MOULOUD ADEL
- Institut FRESNEL, UMR-CNRS 6133, Equipe GSM, France
| | - DANIEL ZUWALA
- Interface Physique-Biologie-Médecine, case EC1, Domaine Universitaire de Saint-Jérôme, 13397 Marseille Cedex 20, France
| | - MONIQUE RASIGNI
- Interface Physique-Biologie-Médecine, case EC1, Domaine Universitaire de Saint-Jérôme, 13397 Marseille Cedex 20, France
| | | |
Collapse
|
11
|
Panetta K, Zhou Y, Agaian S, Jia H. Nonlinear unsharp masking for mammogram enhancement. ACTA ACUST UNITED AC 2011; 15:918-28. [PMID: 21843996 DOI: 10.1109/titb.2011.2164259] [Citation(s) in RCA: 141] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This paper introduces a new unsharp masking (UM) scheme, called nonlinear UM (NLUM), for mammogram enhancement. The NLUM offers users the flexibility 1) to embed different types of filters into the nonlinear filtering operator; 2) to choose different linear or nonlinear operations for the fusion processes that combines the enhanced filtered portion of the mammogram with the original mammogram; and 3) to allow the NLUM parameter selection to be performed manually or by using a quantitative enhancement measure to obtain the optimal enhancement parameters. We also introduce a new enhancement measure approach, called the second-derivative-like measure of enhancement, which is shown to have better performance than other measures in evaluating the visual quality of image enhancement. The comparison and evaluation of enhancement performance demonstrate that the NLUM can improve the disease diagnosis by enhancing the fine details in mammograms with no a priori knowledge of the image contents. The human-visual-system-based image decomposition is used for analysis and visualization of mammogram enhancement.
Collapse
Affiliation(s)
- Karen Panetta
- Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USA
| | | | | | | |
Collapse
|
12
|
Langarizadeh M, Mahmud R, Ramli AR, Napis S, Beikzadeh MR, Rahman WEZWA. Improvement of digital mammogram images using histogram equalization, histogram stretching and median filter. J Med Eng Technol 2011; 35:103-8. [PMID: 21204610 DOI: 10.3109/03091902.2010.542271] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Breast cancer is one of the most important diseases in females worldwide. According to the Malaysian Oncological Society, about 4% of women who are 40 years old and above are involved have breast cancer. Masses and microcalcifications are two important signs of breast cancer diagnosis on mammography. Enhancement techniques, i.e. histogram equalization, histogram stretching and median filters, were used to provide better visualization for radiologists in order to help early detection of breast abnormalities. In this research 60 digital mammogram images which includes 20 normal and 40 confirmed diagnosed cancerous cases were selected and manipulated using the mentioned techniques. The original and manipulated images were scored by three expert radiologists. Results showed that the selected methods have a positive significant effect on image quality.
Collapse
Affiliation(s)
- M Langarizadeh
- Faculty of Medicine and Health Science, University Putra Malaysia, Kuala Lumpur 43400, Malaysia.
| | | | | | | | | | | |
Collapse
|
13
|
Bustince H, Barrenechea E, Fernandez J, Pagola M, Montero J, Guerra C. Contrast of a fuzzy relation. Inf Sci (N Y) 2010. [DOI: 10.1016/j.ins.2009.12.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
14
|
Jinshan Tang, Rangayyan R, Jun Xu, El Naqa I, Yongyi Yang. Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances. ACTA ACUST UNITED AC 2009; 13:236-51. [DOI: 10.1109/titb.2008.2009441] [Citation(s) in RCA: 375] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
15
|
A Self-tuning of Membership Functions for Medical Diagnosis. ENTERP INF SYST-UK 2009. [DOI: 10.1007/978-3-642-01347-8_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
16
|
Papadopoulos A, Fotiadis DI, Costaridou L. Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques. Comput Biol Med 2008; 38:1045-55. [PMID: 18774128 DOI: 10.1016/j.compbiomed.2008.07.006] [Citation(s) in RCA: 84] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2006] [Revised: 06/02/2008] [Accepted: 07/09/2008] [Indexed: 11/28/2022]
Abstract
In this work, the effect of an image enhancement processing stage and the parameter tuning of a computer-aided detection (CAD) system for the detection of microcalcifications in mammograms is assessed. Five (5) image enhancement algorithms were tested introducing the contrast-limited adaptive histogram equalization (CLAHE), the local range modification (LRM) and the redundant discrete wavelet (RDW) linear stretching and shrinkage algorithms. CAD tuning optimization was targeted to the percentage of the most contrasted pixels and the size of the minimum detectable object which could satisfactorily represent a microcalcification. The highest performance in two mammographic datasets, were achieved for LRM (A(Z)=0.932) and the wavelet-based linear stretching (A(Z)=0.926) methodology.
Collapse
Affiliation(s)
- A Papadopoulos
- Department of Medical Physics, Medical School, University of Ioannina, GR 45110 Ioannina, Greece
| | | | | |
Collapse
|
17
|
Yun WL, Rajendra Acharya U, Venkatesh Y, Chee C, Min LC, Ng E. Identification of different stages of diabetic retinopathy using retinal optical images. Inf Sci (N Y) 2008. [DOI: 10.1016/j.ins.2007.07.020] [Citation(s) in RCA: 135] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
18
|
|
19
|
Ng E, Acharya UR, Keith LG, Lockwood S. Detection and differentiation of breast cancer using neural classifiers with first warning thermal sensors. Inf Sci (N Y) 2007. [DOI: 10.1016/j.ins.2007.03.027] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
|
20
|
|
21
|
Jiang J, Yao B, Wason AM. Integration of fuzzy logic and structure tensor towards mammogram contrast enhancement. Comput Med Imaging Graph 2005; 29:83-90. [PMID: 15710543 DOI: 10.1016/j.compmedimag.2004.06.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2004] [Revised: 06/01/2004] [Accepted: 06/01/2004] [Indexed: 11/23/2022]
Abstract
In this paper, we describe a combined approach with fuzzy logic and structure tensor towards improved enhancement of possible MCs (microcalcifications) in digital mammograms. The proposed contrast enhancement algorithm has two operational components. One is structure tensor operator and the other is fuzzy enhancement operator, both of which are arranged in parallel to process the input digital mammograms. While the structure tensor operator processes the digital mammograms and produces a corresponding eigen-image to highlight the region-of-interests, the fuzzy enhancement operator fuzzifies the mammogram via the maximum fuzzy entropy principle in fuzzy domain. As a result, the local fuzzy contrast can be extracted and modified adaptively in accordance with the information provided by the eigen-image, and those non-MCs regions are suppressed by being taken as noise. After that, the mammogram is transformed back to the pixel domain and the enhanced mammogram is constructed. Extensive experimental results show that our proposed algorithm outperforms the existing benchmark in terms of cost figures across the whole range of true positive fractions (TPF).
Collapse
Affiliation(s)
- Jianmin Jiang
- School of Informatics, University of Bradford, BD7 1DP Bradford, UK.
| | | | | |
Collapse
|