 Open Access
 Authors : Dr. B H. Nagarajasri
 Paper ID : IJERTV10IS070009
 Volume & Issue : Volume 10, Issue 07 (July 2021)
 Published (First Online): 10072021
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Design of Mass Detection Algorithm using Hyper Analytical Wavelet Transform in Digital Mammography
Dr. B H. Nagarajasri
InCharge Director
Sri Venkateswara University Computer Centre, Tirupati
Abstract: Breast Cancer Diagnosis and Prognosis are two Gabor filter for texture feature extraction in Segmentation Medical applications that pose a great challenge to the Feature Texture Analysis and automatic ROI(Region of researchers in Radiology and Computer Science. Digital Interest)using threshold OTSU algorithm for calculating
Mammography plays a vital role for classifying tumors in
texture measure of mammograms and mass density
breast cancer diagnosis. Wavelet transformation is one of calculation based on texture measures and to calculate mass
mammograms. The proposed Mass Detection technique for
mammograms. The proposed Mass Detection technique for
the most effective mathematical tool for analyzing the density based on the above features. Application of fuzzy c
detection of breast cancer usingdigital image processing and by
means clustering the mass density based on distribution of
using threshold CLAHEwhich can enhance the image, denoise pixels and to perform Circular transformation in
image the based on wavelet transformation. The result of this technique is ensured by the ability of different wavelet transform coefficients and filtering this coefficients will separate
detection of mass shape, round ,oval
,lobular and irregular shape and to train neuro fuzzy classifier with mass features in comparison with edge
the unnecessary noise and give useful image. These coefficients detection algorithm and detect the tumors in an image.
are then calculated with 2D Discrete wavelettransformation for
different components and to compare 2DDWT and Hyper Analytical wavelet transformation by using geometric feature extraction with Gabor filter for texture feature extraction in Segmentation. Feature Texture Analysis and automatic
PROCESSINMAMMOGRAMIMAGEANALYSIS
Digital mammograms are very useful in detecting microcalcifications to diagnose breast cancer at an earlystage.
ROI(Region of Interest)using threshold OTSU algorithm for Image Processing Technique such as contrast image
calculating texture measure of mammograms and mass density
enhancement, noise removal, segmentation, feature extraction,
calculation based on texture measures and to calculate mass shape analysis can be done using digital mammograms.
density based on the above features. Application of fuzzy c means clustering the mass density based on distribution of pixels and to perform Circular transformation in detection of mass shape like round, oval, lobular and irregular shapes and to train Neuro Fuzzy Classifier with mass features in comparison withedge detection algorithm and detect the tumors in an image..
1.1INTRODUCTION
The advancement in biomedical imaging has opened new doors towards. recognition of breast cancerby providing quantitative image substitutes. Various imaging modalities and their refinements have encompassed for the utilization of wavelets leading to methods for quantitatively analyze structural and functional deformalities in the breast image assessment methods there are based on adaptable computational techniques. The proposed Mass Detection technique for detection of breast cancer using digital image processing and by using threshold CLAHE which can enhance the image, image denoises image the based on wavelet transformation. The result of this technique is ensured by the ability of different wavelet transform coefficients and filtering this coefficients will separate the unnecessary noise and give useful image calculate with 2D Discrete wavelet transform for different components and to compare 2DDWT and Hyper Analytical wavelettransform by using geometric feature extraction with
Wavelet Analysis of Mammogram Images
The given mammogram image is decomposed into multiresolution hierarchy of localized information at different spatial frequencies. Multiscale wavelet representation suggests a mathematical coherent basis not only for existing multigrid techniques but also for exploiting nonlinear systems. Multi resolution wavelet analysis provides a natural hierarchy in which the embedded programme provides an interactive paradigm for accomplishing scalespace feature analysis.
Key Features of Wavelet Analysis of Mammogram
The generalized wavelet transformation algorithms proposed by Ebrahim Jelvehfard, Shradhananda,[3],[5] based on 2DDWT has poor directionality, shift variance, absence of phase information which is serious drawbacks to make reliable or appropriate tool for the radiologist. The above existing drawbacks of the proposed of novel contribution in Hyper Wavelet transformation (HWT) increases better shift variance, good directionality enhancement and alsomaintains linear combination of coefficients details of four 2D DWT sub bands which leads to the development of a reliable tool and help in critical decision making for the Radiologist in Breast cancer Diagnosis.

Hyper Analytical Transformation
The proposed Mass detection algorithm usingHyper Analytical Transformation follows:
Fig. 1.1 Flowchart for Mass Detection
Fig. 1.2 Flowchart Calcification
IMAGEPREPROCESSING
Enhancement of Mammogram Image
Millions of pictures ranging from biomedical images to the image natural surroundings and activitiesenrich our daily visual experience. It is essential to increase dynamic features in the image to provide qualification images. Image adjustment is usually achieved by contrast enhancement using image histogram measures.
Mainly, image enhancement includes intensity and contrast manipulation, noise reduction, background removal, edges sharpening, filtering, etc. The task of mammogram enhancement is to sharpen the edges or boundaries of ROIs, or to increase the contrast between ROIs and background [1] [Balakumaran] et al. It is well known that if a region differs in luminance from its surroundings by less than 2%, it is indistinguishable to human eye. Although micro calcifications are brighter than their surroundings, the contrast for some micro calcifications in a dense breast is quite low that cannot be detected by human eyes. The aim of contrast enhancement is to increase the contrast of microcalcifications over the threshold and help identify the minute microcalcifications.
The first step in the study of mammogram images image enhancement is denoising the image. The denoising technique should not affect or destroy information content in the image. The removal of background noise while preserving edge information of suspicious area in mammograms is the intended objective.
Various enhancement techniques viz., histogram equalization neighborhood based contrast enhancement algorithm, selective Median Filtering enhancement method based on multi scale analysis have been done enhancement of contrast of mammogram images.
Contrast enhancement by using THRESHOLD CLAHE algorithm
The contrastlimited adaptive histogram equalization (CLAHE) produces images in which the noise content of an image is not enhanced, but in which sufficient contrast is provided for the visualization of structures within the image. Images processed with CLAHE have a more natural appearance and facilitate the comparison of different areas of an image. However,the reduced contrast enhancement of CLAHE may hinderthe ability of an observer to detect the presene of somesignificant grayscale contrast.
Threshold CLAHE operates on small regions in the image, called tiles, rather than the entire image. Each tiles contrast is enhanced, so that the histogram of the output region approximately matches the histogram specified by the Distribution parameter. The neighboring tiles are then combined using bilinear interpolation to eliminate artificially induced boundaries. The contrast, especially in homogeneous areas, can be limited to avoid amplifying any noise that might be present in the image.
Proposed Threshold CLAHEAlgorithm
Threshold CLAHE method helps in increase ofglobal contrast of a image compared with histogramequalization method, The CLAHE method being anadaptive algorithm, works for multi resolution images Objective: Threshold CLAHE technique is applied onoriginal input mammogram to enhance the contrast.
Algorithm:
Step 1: Threshold CLAHE technique is applied on originalinput mammogram to improve the contrast.
Step2: Hyper analytical wavelet transform (HWT) is applied on the output obtained in the first step, then the segmentation at various scales (multi resolution) to detect microcalcifications and otherabnormalities.
Step 3: The proposed nonlinear complex diffusion based unsharp masking and crispening method is appliedon the enhanced mammogram
Step 4: The proposed modified threshold CLAHE basedimage segmentation is applied on mammograms obtained in step 3.
Threshold calculation Maxval = Maximum values
Num_bins = Number of histogram binsmu_t
= Qumulative threshold value
Effectiveness metric of mammogram calculated by Em = maxval/(sum(p.*((1:num_bins).^2)) – mu_t^2);
So Effectiveness metric maybe used but for large scale processing of mammograms computational complexity may increase.
Figure: 1.3 Results Threshold CLAHEAlgorithm
Noise Modeling
The goal of denoising the image is to use appropriate techniques of refining the images so that the resultant image would have a better visual quality free from aberrations and noises.
Noise Sources
Noise modeling in images is affected by capturing devices through data transmission media, image quantization and discrete source of radiation. The characteristics of noise depends on its source.
Noise Removal based on wavelet transform
Wavelet transform plays an important role in biomedical image processing. Wavelets first introduced in medical imaging research in 1991in a journal paper describes an application of wavelet transforms for noise reduction in MRI images. A special issue of IEEE Transactions in Medical imaging, provides a large collection of most recent research work on wavelets in medical image processing.
Proposed Noise Removal Algorithm:

Read the mammogram

By using preprocessing technique resize the image

Enhancing the image using Threshold CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm and compared with Histogram equalization and their performance.

Apply Mean and Median filters on the image for the removal of digitization noise to acquire the filtered image. Apply Adaptive hybrid Bilateral Filter to obtain the filtered image

Calculating features of image
It is challenging process for researchers toeliminate noise from the noisy image to get the originalimage because it introduces artifacts (blurring ringing effect staircase effect and checkerboard effect) in the images.
It is very important to identify the noise presentin the image to select appropriate denoising algorithm. If the image is corrupted with salt and pepper noise the filtering methods perform better. The wavelet based approach is useful if the image corrupted with Gaussian noise. So the denoising algorithm is application dependent. The image denoising algorithms can be broadly classified into spatial domain filtering methodsand Transform domain filtering methods.
Traditional Smoothing Filters such as Mean, Median Filters are normally employed on spatial domain. Various enhancement techniques, viz., histogram equalization, neighborhood based contrast enhancement algorithm, selective Median filtering enhancement method based on multi scale analysis etc. may be used for enhancing the contrast of mammogram images. Mammographic image utilizes a narrow range of gray
levels, without a welldefined histogram structure. Thus the conventional histogram equalization techniques may not be suitable in enhancing the mammogram images.
Figure: 1.4 Mean Filter Median Filter
Standard deviation Sigma=5 
Standard deviation Sigma=10 
Standard deviation Sigma=15 
Standard deviation Sigma=20 

PSNR 
MSE 
PSNR 
MSE 
PSNR 
MSE 
PSNR 
MSE 

Noisyimage 
24.89 
7.69 
25.66 
7.36 
25.98 
7.13 
26.39 
7.01 
DWT 
26.36 
6.99 
26.74 
6.63 
26.83 
6.22 
27.06 
6.03 
HWT 
27.36 
5.19 
27.43 
4.97 
27.52 
4.86 
27.96 
4.63 
The wavelet transformations are characterized by two features the Mother wavelet MW and primary resolution PR number iterations. An appealing particularity of the 2D is the inter scale dependency of the wavelet coefficients. The main advantage of the implementation of 2D DWT is its flexibility as it inheritsfrom ID DWT, Daubechies, Symmlet or Coiflet family.
Adaptive hybrid bilateral filter
The main advantage of the classical bilateral filtering method is that it considers both the spatial locality and neighboring points with similar amplitudes at the same time which make it can better preserving theimage edges and textures than the conventional linear filtering algorithms.
We propose the Adaptive hybrid bilateral filter that smoothes the pictures by conserving the edges, suggests a nonlinear combination of close image values. Although bilateral filtering may be a nonlinear technique its non iterative, local and straightforward. The nonlinearity arises attributes to the nonlinear relationship of picture element values of a picture. Our proposed bilateral filters comprise of 2 element filters: a domain filter and a variety filter. Domain filter element refers to the normal lowpass filter that provide average values ofthe image. The implementation of the domain filter utilizes a Gaussian blur kernel for filter weights. This adaptive quality is illustrated within the figures below. Bilateral filter is the resultant product of the domain and vary filters, which ends in an averaging of image pixels supported specially and measures closeness. This is often the central plan underlying the bilateral filtering interpolation technique.
Later we apply smoothing process to the pixels
that are a unit in shut geometric proximity having similar contents. Thus, it can be assumed to safe the average over shut pixels. However, this central plan breaks down at the edges of a picture. During this context, edges ask those points on a picture wherever there are unit discontinuities or sharp contrasts between pixels contentand its immediate neigh bur. The bilateral filter accountsfor the sides by weight pixels supported their
photometrical similarity additionally to geometricproximity.
ProposedAdaptive Hybrid Bilateral Filter
Step1:precompute Gaussian distance Exp ( (x^2+y^2)/2*sigma_d^2)
Step2: Apply Bilateral Filter having threshold as max.limit. Sep3: Extract the local Region
Step4: Compute Gaussian intensity weight H=exp ((I(A (ii, j) ^ 2/2*Sigma_r^2)
Step5: Calculate filter Response
Figure: 1.5 results of Adaptive hybrid Bilateral Filter
Performance criteria
The Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) are the two metrics used to compare image processing (denoising) quality. The MSErepresents the cumulative squared error between the denoised and the original image, whereas PSNR represents a measure of the peak error. The lower the value of MSE, the higher is value of the PSNR. The Mean Square Error (MSE) is calculated by using the followingequation 1.1,
(1.1)
Where Ii is input, noise free image Io is the output
image may be noisy or denoised and M, N are thenumber of rows and columns of the image respectively.
Then, the Peak Signal to Noise Ratio (PSNR) isbeing computed using the following equation 1.2
(1.2)
Where, R is themaximum fluctuation in the inputimage data type. For example, if the input image has a double precision floatingpoint data type, then R is 1. Ifit has an 8bit unsigned integer data type, R is 255, and so on.
Root mean square error is computed to measure the image enhancement Application.
Adaptive Bilateral smoothes pictures whereas conserving edges suggests nonlinear combination of close image values. Our proposed bilateral filter comprises of two element filters, a dominant filter and avarietyfilter.
Bilateral Filter is the product of domain and a varietyfilter.
It is observed that using Adaptive Bilateral filter which increases the PSNR value and decreases the MSE
.ENL equivalent number of looks one of the good approach estimating speckle noise levels, is to measure ENL over uniform image region. Larger value of ENL corresponds to better suppression of speckle. The valueof ENL also depends on the size of tested region.
2D DWT based thresholding
The wavelet transformations are characterized by two features the Mother wavelet MW and primary resolution PR number iterations. An appealing particularity of the 2D is the inter scale dependency of the wavelet coefficients. The main advantage of the implementation of 2D DWT is its flexibility as it inheritsfrom ID DWT, Daubechies, Symmlet or Coiflet family.
Discrete wavelet transform (DWT) for an imageas a 2D signal can be derived from 1D wavelet trans form. The easiest way to get the 2D scale and wavelet function is multiplying two 1D functions. The 2D scale function is achieved by multiplying two scale functions as below
wavelet functions are accomplished by multi plying two wavelet functions or scale and wavelet functions.
The implementation of an analysis filter bank for a single level 2D DWT is shown in figure. This struc ture produces three detailed subimages (HL, HL, HH) corresponding to three different directionalorientations (Horizontal, Vertical and Diagonal) and a lower resolu tion subimage LL. The filter bank structure can be iter ated in a similar manner on the LL channel to provide multilevel decomposition.
(1.3)
Equivalent number of looks computed to measure the image enhancement Application.
(1.4)
Mean filter 
Median filter 
Adaptive hybrid bilateral filter 

MSE 
20.5 
18.2 
16.3 
PSNR 
55 
58 
60 
RMSE 
7.75 
1.0833 
0.2039 
Enl 
0.0033 
0.0021 
1.198 
Mean filter 
Median filter 
Adaptive hybrid bilateral filter 

MSE 
20.5 
18.2 
16.3 
PSNR 
55 
58 
60 
RMSE 
7.75 
1.0833 
0.2039 
Enl 
0.0033 
0.0021 
1.198 
Table 1.1 Performance of Mean, Median, AdaptiveHybrid Bilateral Filters
Figure: 1.6 Single level analysis filter bank for 2DDWT
IMAGE
DWT
on ROWS
DWT
on
COLUMNS
L H
HyperAnalytical Wavelet Transform
LL(a) 
HL(d2) 
LH(d1) 
HH(d3) 
LL(a) 
HL(d2) 
LH(d1) 
HH(d3) 
These can be overcome by introducing one of the following analytical wavelet transform called Hyper analytic wavelet transform.
Figure: 1.7 Block Diagramof DWT (a) OriginalImage (b)
Output image after the 1D applied on Rowinput(c) Output image after the second 1Dapplied on
column input
igure : 1.13 Implementation of HWT
igure : 1.13 Implementation of HWT
Figure: 1.8 Multilevel decompositionhierarchyof an image witpDDWT
Figure: 1.9 Results illustrating 2D DWT waveletdecomposition
F
Figure : 1.12 The absolute values of the spectra of horizontal and diagonaldetail sub images obtained after the first iterations of 2D DWT and HWT.In the HWT case, the real and imaginary parts of complex coefficients are separated
Figure: 1.10 2DDWT horizontal details Figure: 1.11 2DDWT vertical details
The main disadvantage of 2D DWT are the poor directional selectivity and shift sensitivity. Separable filtering along the rows and columns of the image produces four images at each level. The LH and HL band pass sub images can select mainly horizontal and vertical edges respectively but the HH subimage components from diagonal features of either orientation.
MASS shape calculation
Image Samples 
Mam1 
Mam2 
Mam3 
Mam3 
Mam5 
Area 
5 
8 
6 
9 
5 
Perimeter 
189 
177 
169 
188 
192 
Max radius 
121.73 
101.36 
136.90 
119.61 
122.01 
Min.radius 
3.753 
3.188 
3.999 
3.333 
5.021 
Eccentricity 
9.933 
9.633 
8.933 
7.933 
9.996 
Equivdiameter 
2.256 
2.566 
1.255 
2.666 
2.996 
Eelongatedness 
1.812 
1.916 
2.762 
2.832 
2.976 
Entropy 
9.998 
9.118 
8.298 
9.971 
8.889 
Circularity1 
3.296 
3.299 
3.796 
3.299 
3.797 
Circularity 2 
1.976 
1.763 
2.977 
2.976 
1.666 
Compactness 
0.0329 
0.035 
0.066 
0.053 
0.033 
Dispersion 
23.33 
22.86 
21.55 
23.09 
23.66 
Thinness 
3290 
3100 
2920 
3085 
3211 
Standard Deviation 
0.123 
0.133 
0.167 
0.183 
0.133 
Image Samples 
Mam1 
Mam2 
Mam3 
Mam3 
Mam5 
Area 
5 
8 
6 
9 
5 
Perimeter 
189 
177 
169 
188 
192 
Max radius 
121.73 
101.36 
136.90 
119.61 
122.01 
Min.radius 
3.753 
3.188 
3.999 
3.333 
5.021 
Eccentricity 
9.933 
9.633 
8.933 
7.933 
9.996 
Equivdiameter 
2.256 
2.566 
1.255 
2.666 
2.996 
Eelongatedness 
1.812 
1.916 
2.762 
2.832 
2.976 
Entropy 
9.998 
9.118 
8.298 
9.971 
8.889 
Circularity1 
3.296 
3.299 
3.796 
3.299 
3.797 
Circularity 2 
1.976 
1.763 
2.977 
2.976 
1.666 
Compactness 
0.0329 
0.035 
0.066 
0.053 
0.033 
Dispersion 
23.33 
22.86 
21.55 
23.09 
23.66 
Thinness 
3290 
3100 
2920 
3085 
3211 
Standard Deviation 
0.123 
0.133 
0.167 
0.183 
0.133 
Table 1.5: Performance evaluation of mass shape
Figure: 1.14 HWT horizontal details Figure: 1.15 HWT vertical details
Figure: 1.16 HWT diagonal details
Table 1.4 Preferred directions of HWT
The below represented mammographic image namely mass shape round, oval, lobular, and irregular are appropriately proposed in calculation of texture measures mass for the mammogram images.
Mass Shape Round Mammogram Result For Round Mammo
Mass Shape Oval Mammogram Oval Mammogram Result
Advantages of HWT
Lobular Mammogram Result Lobular Mammogram
Figure: 1.17 Illustrations of directional coeffiecients of HWT
Figure: 1.18 HWT Plot in bar
graphical representation Irregular Mammogram Result For Irregular Mammogram
Mass Density Clasification Using Texture Measures
Table 1.6 : Texture measures mass density for the mammogram images
Image Samples 
Average Intensity 
Standard Deviation 
Entropy 
Smoothness 
Third Moment 
Uniformity 
Mam1 
23.6300 
3.3189 
5.8317 
0.9168 
2.8595 
0.5309 
Mam2 
33.1200 
13.9318 
6.6575 
0.9955 
0.8633 
0.2177 
Mam3 
27.3300 
10.3903 
7.0973 
0.9908 
1.9011 
0.2527 
Mam3 
32.0800 
5.8133 
7.3255 
0.9713 
5.7375 
0.1537 
Mam5 
33.3800 
2.8373 
5.5573 
0.8895 
2.3276 
0.5803 
Mam6 
15.3600 
5.3993 
6.7259 
0.9668 
1.0066 
0.2673 
Mam7 
22.2300 
8.5673 
6.0523 
0.9866 
2.0387 
0.3637 
Mam8 
18.1600 
2.5137 
5.3763 
0.8633 
2.8109 
0.3639 
Mam9 
22.3000 
12.3672 
6.5677 
0.9935 
2.2260 
0.2738 
Mam10 
33.2300 
3.5868 
5.8397 
0.9536 
2.7132 
0.5527 
Table 1.7 Classification of mammogram images basedon texture features
CLASSIFICATIONUSINGNEUROFUZZY
In the field of artificial intelligence, neurofuzzyrefers to combinations of artificial neural networks andfuzzy logic.
The authors [38], [116], proposed various methodsbased on 2D Wavelet Discrete wavelet transform which considers horizontal and vertical edges neglecting diagonal edges leading to inappropriate diagnosis as it does not take microcalcification which are present in diagonals into consideration.
The proposed mass detection algorithm focus on enhancing shift variance and directional selectivity.
The enhancement of the directional selectivity of the HWT is made through linear combinations of detail coefficients belonging to each sub band of each of thefour 2D DWTs.
1.5. EVALUATIONOFPROPOSEDFUZZYRULETable
Mass type 
No. of rules 
Rule depth 
Features used by rules 
Classification using Fuzzy Rules (%) 
I,O 
8 
1 
En, Esd, Rmax, SI, SD, Dp 
96.99 
I,L 
8 
1 
Entpy, En, Rmin, Peri, CN, C2, SI 
93.06 
I,R 
2 
3 
Peri, En 
93.09 
O,R 
2 
3 
CN, ECT, Rmin,C2,Area,Peri 
98.63 
L,O 
3 
1 
Dp, Area, Rmin 
95.06 
L,R 
3Ta 
1 
Peri, C2 
95.45 
I, L,R 
6 
1 
Eqd, SD, Rmin, CN, ECT 
94.36 
I,L,O 
12 
1 
Esd,CN,Entpy,SD,DP,ECT, SI,C2,Area 
93.99 
L,O,R 
6 
1 
Peri,Rmin,DP,EULN 
86.03 
I,O,R 
8 
1 
SD,Peri,En,Rmax,EULN,C N,DP, Area 
89.33 
I,L,O,R 
12 
6 
Esd,Rmin,CN,En,SI 
88.36 
I,L,O,R 
2 
8 
Esd,Rmin,CN,SI,En 
93.06 
Mass type 
No. of rules 
Rule depth 
Features used by rules 
Classification using Fuzzy Rules (%) 
I,O 
8 
1 
En, Esd, Rmax, SI, SD, Dp 
96.99 
I,L 
8 
1 
Entpy, En, Rmin, Peri, CN, C2, SI 
93.06 
I,R 
2 
3 
Peri, En 
93.09 
O,R 
2 
3 
CN, ECT, Rmin,2,Area,Peri 
98.63 
L,O 
3 
1 
Dp, Area, Rmin 
95.06 
L,R 
3Ta 
1 
Peri, C2 
95.45 
I, L,R 
6 
1 
Eqd, SD, Rmin, CN, ECT 
94.36 
I,L,O 
12 
1 
Esd,CN,Entpy,SD,DP,ECT, SI,C2,Area 
93.99 
L,O,R 
6 
1 
Peri,Rmin,DP,EULN 
86.03 
I,O,R 
8 
1 
SD,Peri,En,Rmax,EULN,C N,DP, Area 
89.33 
I,L,O,R 
12 
6 
Esd,Rmin,CN,En,SI 
88.36 
I,L,O,R 
2 
8 
Esd,Rmin,CN,SI,En 
93.06 

Evaluation of proposed fuzzy rule
Table 1.9 Evaluation of 2DDWTAND HWT

6. CONCLUSION

The proposed Mass Detection Technique for breast cancer using digital image processing and using threshold CLAHE algorithm which can lead to an enhanced image calculated with 2D DWT for different components and to compare 2D DWT and HWT using geometric extraction with gabor filter for texture feature extraction in SFTA (Segmentation Feature Texture Analysis ) and automatic ROI using OTSU thresholding for calculating texture measure of mammograms and mass density calculation using the above features. Applying fuzzy cmeans clustering in mass density based on distribution of pixels and to apply circular transform to detect mass shape, and to train neuro fuzzy classifier with mass features pave way for fruitful results in the detection of tumors comparison to edge detection algorithm.
Finally the experiment results show that the proposed methodology of mass detection algorithm to maintain optimum diagnosis to enhance accuracy improve the poor directionality and selectivity for massdetection. This helps in the early detection of breast cancer that leads to premature control and disclose manypossible solutions.
1.7. REFERENCES

Balakumaran T Ila.Vennila, and C. Gowrishankar, Detection of Microcalcification in Digital Mammograms using One Dimensional Wavelet Transform, ICT ACT Journal on Image and Video Processing, no. 2, pp. 99 104, November 2010.

Dengler JS. Behrens, Segmentation of microcalcifications in mammograms, IEEE Trans.

Ebrahim Jelvehfard1,, Karim Faez, Afsane Laluie, Microcalcification Detection in Mamography Images Using 2D Wavelet Coefficients Histogram, 2013

Milosevic, Marina,Segmentation for the enhancement of microcalcifications in digital mammograms vol22, no.52014 pp701 715

Shradhananda Beura, Banshidhar Majhi and Ratnakar Dash, Mammogram Classification using Two Dimensional Discrete Wavelet Transform and Gray Level Cooccurrence Matrix for Detection of Breast Cancer, 2014.