A Polynomial filtering model for enhancement of mammogram lesions

Bhateja, Vikrant; Urooj, Shabana; Misra, Mukul; Pandey, Ankita; Lay-Ekuakille, A.

doi:10.1109/memea.2013.6549714

Cited by 32 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The researches done with CAD tools on mammograms has limited its scope to the enhancement part for detection of [10].Fuzzy Logic has also been used for the same [11][12] [13].However approach has been made to quantify the enhancement techniques [14].Our paper proposes the use of fuzzy Logic as a tool for efficient quantification of the different enhancement techniques(equalization enhancement,edge sharpening, non-linear enhancement, wavelet enhancement, local statistics enhancement).…”

Section: Literature Reviewmentioning

confidence: 99%

Mammographic Image Enhancement Analysis Using Fuzzy Logic

2016

IJAERD

View full text Add to dashboard Cite

Breast cancer figures among the leading causes of cancer related deaths among women. Detection of cancer at the initial stages increases the probability of survival of the patient. For this detection, mammography is done. However most of the cases go unnoticed either due to lower contrast variations or due to noise in mammogram images.The main objective of image enhancement is to improve desired characteristics of an image and make it visually better. This paper proposes a method for quantifying the various enhancement techniques used for cancer detection in mammogram images. The paper proposes the use of fuzzy logic to realize this objective. First the mammogram is enhanced using different enhancement techniques. In next step, the frequency domain comparison of the original image and each of the enhanced image is performed. Using this comparison we obtain the variation in the frequencies of the enhanced image as compared with the original image. Finally these variations are then quantized using the fuzzy sets to provide us the merit of the different techniques, of their degree of enhancement.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Mammographic Image Enhancement Analysis Using Fuzzy Logic

2016

IJAERD

View full text Add to dashboard Cite

show abstract

“…A systematic theory of invariant moment for pattern recognition can be found in [6]. A Non-Linear Approach to ECG Signal Processing using Morphological Filters [16] [19] . Effective evaluation of tumor region in brain MR images using hybrid segmentation is discussed in [18].…”

Section: Introductionmentioning

confidence: 99%

Rotational-Invariant Texture Analysis Using Radon and Polar Complex Exponential Transform

Singh

Urooj

Ekuakille

2015

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Abstract. Rotational invariant texture analysis technique using Radon and PCET is proposed in this stab. Rotation invariance is achieved within the Radon space. Translation invariance is achieved by normalization of moment of PCET. A k-nearest neighbor classifier is employed for classifying the texture. To test and evaluate the proposed method several sets of texture was evaluated. The evaluation is achieved with different scaling, translation and rotation under different noisy conditions. Correct classification percentage is calculated for various noise conditions. Experimental result shows pre-eminence of the employed method as compared to the recent invariant texture analysis methods.

show abstract

“…Hence, the wavelets preserve time and frequency information, thereby yielding a suitable approach for medical image fusion. Further, these fused images are also processed with denoising [19]- [20], contrast [21]- [30] and edge enhancement [31]- [33] techniques to improve upon the visualization of diagnostic information. The proposed work therefore presents a combination of wavelet transform and PCA as an improvement to the aforementioned .com limitations.…”

Section: Introductionmentioning

confidence: 99%

Medical image fusion using combination of PCA and wavelet analysis

Krishn

Bhateja

Himanshi

et al. 2014

2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

View full text Add to dashboard Cite

Medical image fusion for merging of complementary diagnostic content has been carried out in this paper using Principal Component Analysis (PCA) and Wavelets. The proposed fusion approach involves sub-band decomposition using 2D-Discrete Wavelet Transform (DWT) in order to preserve both spectral and spatial information. Further, PCA is applied on the decomposed coefficients to maximize the spatial resolution. An optimal variant of the daubechies wavelet family has been selected experimentally for better fusion results. Simulation results demonstrate an improvement in visual quality of the fused image in comparison to other state-of-art fusion approaches.

show abstract

A Polynomial filtering model for enhancement of mammogram lesions

Cited by 32 publications

References 18 publications

Mammographic Image Enhancement Analysis Using Fuzzy Logic

Mammographic Image Enhancement Analysis Using Fuzzy Logic

Rotational-Invariant Texture Analysis Using Radon and Polar Complex Exponential Transform

Medical image fusion using combination of PCA and wavelet analysis

Contact Info

Product

Resources

About