Mammography Images Segmentation via Fuzzy C-mean and K-mean

Kamil, Mohammed Y.; Salih, Ali Mohammed

doi:10.22266/ijies2019.0228.03

Cited by 35 publications

(23 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The GLCM features are analysed in this work, so as to characterize the lung CT images. The variation between the normal image and cancer image, clearly reveal the capability of this method to classify the normal image from the cancer image [23][24][25].…”

Section: Resultsmentioning

confidence: 99%

Development of algorithm for identification of maligant growth in cancer using artificial neural network

Pandian

Ravikumar

Kumar

2020

IJECE

View full text Add to dashboard Cite

The precise identification and characterization of small pulmonary nodules at low-dose CT is a necessary requirement for the completion of valuable lung cancer screening. It is compulsory to develop some automated tool, in order to detect pulmonary nodules at low dose ct at the beginning stage itself. The numerous algorithms had been proposed earlier by many researchers in the past, but, the accuracy of prediction is always a challenging task. In this work, an artificial neural network based methodology is proposed to find the irregular growth of lung tissues. Higher probability of detection is taken as a goal to get an automated tool, with great accuracy. The finest feature sets derived from Haralick Gray level co occurrence Matrix and used as the dimension reduction way for feeding neural network. In this work, a binary Binary classifier neural network has been proposed to identify the normal images out of all the images. The capability of the proposed neural network has been quantitatively computed using confusion matrix and found in terms of classification accuracy.

show abstract

Section: Resultsmentioning

confidence: 99%

Development of algorithm for identification of maligant growth in cancer using artificial neural network

Pandian

Ravikumar

Kumar

2020

IJECE

View full text Add to dashboard Cite

show abstract

“…The mammograms are collected from the mini-MIAS database [21]. This database contains 322 mammograms from 161 women; mini-MIAS include normal and abnormal mammograms, the abnormal mammograms categorized to benign and cancerous.…”

Section: Proposed Methodsmentioning

confidence: 99%

Computer aided diagnosis for breast cancer based on the gabor filter technique

Kamil

2020

IJECE

View full text Add to dashboard Cite

The most prominent reason for the death of women all over the world is breast cancer. Early detection of cancer helps to lower the death rate. Mammography scans determine breast tumors in the first stage. As the mammograms have slight contrast, thus, it is a blur to the radiologist to recognize micro growths. A computer-aided diagnostic system is a powerful tool for understanding mammograms. Also, the specialist helps determine the presence of the breast lesion and distinguish between the normal area and the mass. In this paper, the Gabor filter is presented as a key step in building a diagnostic system. It is considered a sufficient method to extract the features. That helps us to avoid tumor classification difficulties and false-positive reduction. The linear support vector machine technique is used in this system for results classification. To improve the results, adaptive histogram equalization pre-processing procedure is employed. Mini-MIAS database utilized to evaluate this method. The highest accuracy, sensitivity, and specificity achieved are 98.7%, 98%, 99%, respectively, at the region of interest (30×30). The results have demonstrated the efficacy and accuracy of the proposed method of helping the radiologist on diagnosing breast cancer.

show abstract

“…(28) and J2 is adaptive complex diffusion based non linear filter adapted to Rayleigh noise, as explained in section 2.3, which is responsible for reduction of Rayleigh noise from segmented image in each iteration obtained by minimizing modified cost functional of modified FCM algorithm as mentioned above in Eq. (24). The solution of functional J2 obtained after its minimization as explained in section 2.3.…”

Section: The Proposed Model For Segmentation Of Ultrasound Image In Pmentioning

confidence: 99%

“…K means is found to be a better option for exclusive clustering but does not use local spatial statistics of the pixels. Fuzzy c means [22][23][24][25][26][27][28] is a soft clustering method where the division of image into clusters is based on membership function. But FCM method is found to be sensitive to noise.…”

Section: Introductionmentioning

confidence: 99%

A Complex Diffusion Based Modified Fuzzy C- Means Approach for Segmentation of Ultrasound Image in Presence of Speckle Noise for Breast Cancer Detection

Srivastava¹,

Kumar²,

Mishra³

et al. 2020

RIA

View full text Add to dashboard Cite

This paper proposes a single framework for segmentation of abnormalities for breast cancer detection from Ultrasound images in presence of Rayleigh noise i.e. noise removal and segmentation are embedded in single step. It accomplishes dual purpose in a single framework simultaneously for the preprocessing and segmentation. The proposed framework comprises of two terms, first term, is used for segmentation which is a modified fuzzy c-means segmentation (MFCM) approach while second term is an adaptive complex diffusion based non linear filter (ACDPDE) that performs as regularization function for removal of Rayleigh noise, enhancement, and edge preservation of ultrasound Image. The various existing segmentation methods viz. K-Means, Texture based, Fuzzy C-Means (FCM), total variation based FCM (TVFCM), Adaptive fourth order PDE based FCM (AFPDEFCM), and the proposed method are evaluated for 50 sample ultrasound images of breast cancer. The region of interest (ROI) segmented image of ultrasound breast tissue is compared with ground truth images. From the acquired results and its analysis, it is observed that the proposed method is more robust and provides better segmentation result for ultrasound images in terms of various performance measures such as Global Constancy error (GCE), Tanimoto coefficient, Variation of Information (VOI), Probability Random Index (PRI), Jaccard coefficient, accuracy, True Positive Rate (TPR), False Positive Rate (FPR), True Negative Rate (TNR), dice index, False Negative Rate (FNR), and Area under curve (AUC). The proposed approach is capable of handling segmentation problem of blocky artifacts while achieving good tradeoff between Rayleigh noise removal and edge preservation. The proposed method may be useful for finding additional 33% cases of breast cancer which is missed or not detected by mammography.

show abstract

Mammography Images Segmentation via Fuzzy C-mean and K-mean

Cited by 35 publications

References 23 publications

Development of algorithm for identification of maligant growth in cancer using artificial neural network

Development of algorithm for identification of maligant growth in cancer using artificial neural network

Computer aided diagnosis for breast cancer based on the gabor filter technique

A Complex Diffusion Based Modified Fuzzy C- Means Approach for Segmentation of Ultrasound Image in Presence of Speckle Noise for Breast Cancer Detection

Contact Info

Product

Resources

About