Computer Assisted Screening of Microcalcifications In Digitized Mammogram For Early Detection of Breast Cancer Thesis Presentation Nashid Alam Registration No: 2012321028 [email protected]Supervisor: Prof. Dr. Mohammed Jahirul Islam Department of Computer Science and Engineering Shahjalal University of Science and Technology Friday, December 25, 2015 Driving research for better breast cancer treatment “The best protection is early detection” 0 10 20 30 0 20 40 -0.1 0 0.1 5; 0.5; 0.7854 5; 0.5; 0.7854
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Computer Assisted Screening of Microcalcifications In Digitized Mammogram For Early Detection of Breast CancerThesis Presentation
Department of Computer Science and EngineeringShahjalal University of Science and TechnologyFriday, December 25, 2015
Driving research for better breast cancer treatment “The best protection is early detection”
010
2030
020
40-0.1
0
0.1
5; 0.5; 0.7854
5; 0.5; 0.7854
Introduction
Breast cancer:The most devastating and deadly diseases for women.
o Computer aided detection (CADe) o Computer aided diagnosis (CADx) systems
We will emphasis on :
Background Interest
Background Interest
Interest comes from two primary backgrounds
1. Improvement of pictorial information- - For Human Perception
How can an image/video be made more aesthetically pleasing
How can an image/video be enhanced to facilitate:extraction of useful information
Background Interest
Interest comes from two primary backgrounds
2. Processing of data for:Autonomous machine perception- Machine Vision
Micro-calcification
Mammography
Mammogram
Micro-calcification
Background knowledge
Micro-calcification
Micro-calcifications :- Tiny deposits of calcium- May be benign or malignant- A first cue of cancer.
Position:1. Can be scattered throughout the mammary gland, or 2. Occur in clusters.(diameters from some µm up to approximately 200 µm.)3. Considered regions of high frequency.
Micro-calcification
They are caused by a number of reasons:
1. Aging –The majority of diagnoses are made in women over 50
2. Genetic –Involving the BRCA1 (breast cancer 1, early onset) and
BRCA2 (breast cancer 2, early onset) genes
Micro-calcifications Pattern Determines :The future course of the action-
I. Whether it be further investigatory techniques (as part of the triple assessment), or
II. More regular screening
Mammography
Background knowledge
Mammography Machine
Mammography
USE:I. Viewing x-ray imageII. Manipulate X-ray image on a computer screen
Mammography :
Process of using low-energyx-rays to examine the human breast
Used as a diagnostic and a screening tool.
The goal of mammography :The early detection of breast cancer
Mammography Machine
Mammogram
Background knowledge
mdb226.jpg
Mammogram
Mammogram:An x-ray picture of the breast
Use:To look for changes that are not normal.
Result Archive:The results are recorded:
1. On x-ray film or 2.Directly into a computer
mdb226.jpg
Literature Review
To detect micro-calcifications in an automatic manner-A number of methods have been proposed
These include:
Global and local thresholding
Statistical approaches
Neural networks
Fuzzy logic
Thresholding of wavelet coefficients and related techniques.
Literature Review
Literature Review
Camilus et al.(2011)[1] propose an efficient method
To identify pectoral mussel using:
Watershed transformation
Merging algorithm to combine catchment basins
MIAS database(84 mammograms)
Literature Review
Literature Review
Pronoj et al.(2011)[2] reviews on :
Thresholding techniquesBoundary based methodHybrid techniquesWatershed transformationEdge detection:
SobelPrewittRobertsLaplacian of GaussianZero-crossCanny
Goal:oTo improve quality of imageoFacilate further processingoRemove noiseoRemove unwanted part from the background
Literature Review
Oliver et al.(2010)[3] worked on:
Local feature extraction from a bank of filters.
Performs training steps:-To automatically learn and select:
The features of microcalcifications.
Literature Review
Goal:oTo obtain different microcalcification morphology
•The structuring element is moved across every pixel in the original image to give a pixel in a new processed image(very like spatial filtering)
•The value of this new pixel depends on the operation performed
•There are two basic morphological operations:
Erosion and Dilation
Structuring Elements, Hits & Fits
Image Morphology Noise Removing
Erosion of image f by structuring element s is given by f sThe structuring element s is positioned with its origin at (x, y) and the new pixel value is determined using the rule:
Erosion
=otherwise 0
fits if 1),(
fsyxg
Structuring Elements, Hits & Fits
A morphological opening of an image is an erosion followed by a dilation
Dilation of image f by structuring element s is given by f sThe structuring element s is positioned with its origin at (x, y) and the new pixel value is determined using the rule:
⊕
=otherwise 0
hits if 1),(
fsyxg
A morphological closing of an image is a dilation followed by an erosion
After Removing Some NoiseImage Containing Noise(mdb041.jpg)
Noise Removing
Chosen Technique 2D MEDIAN FILTERING FOR SALT AND PEPPER NOISE
I = medfilt2(I, [1 5]); Median filtering is a nonlinear operation often used in image processing to reduce "salt and pepper" noise. A median filter is more effective than convolution when the goal is to simultaneously reduce noise and preserve edges. Since all the mammograms are in high quality images, there is no need to perform median filtering
Why choosing?
2.Image Smoothing/Filtering(Low pass):
1. Morphological AnalysisOVER
-Does not work will on all the image [I = medfilt2(I, [1 5]);] •No effect most of the time•Absence of salt and peeper noise
-Tendency of loosing interesting details
Class: Benign
Image segmentation K-means Clustering
Goal: Removing X-ray Labeling And Pectoral muscles
2.Some details are missing(Details around Edge region )
Image MorphologyGoal: Region filling(Region inside the blob)
X-ray Label Removing
Direct Binarization Without Image enhancement
Original image Result image (Label Removed)
mdb240.jpg
mdb219.jpg
mdb231.jpg
Issues
1.Does not always produce appealing output
2.Some details are missing(Details around Edge region )
mdb212.jpg
mdb214.jpg
mdb219.jpg
mdb226.jpg
Experimental results:
Image MorphologyGoal: Region filling(Region inside the blob)
X-ray Label Removing
Direct Binarization Without Image enhancement
-To find largest blobUse -Otsu’s thresholding technique (graytrash) [20]
-Finding Bi-level the image(im2bw)
To Achieve The Desired Final Result:
-ApplyA Range Of Techniques on original image
[20] Otsu, N., "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.
X-ray Label Removing
X-ray Label Removing
1. Histogram equalization of the original X-ray image2. Adjust image contrast3. Apply Otsu's Thresholding Method [20] and
find bi-level the image which has several blobs in it. 4. Finding the Largest blob (Bwlargest.bolb)5. Hole filling within the blob region6. Keep the true pixel value covering only the area of largest
blob and discard other features from the original image7. X-ray label is successfully removed
Plan of Action
[20] Otsu, N., "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.
Removing pectoral muscleKeeping fatty tissues and ligaments
mdb212.jpg(a)Main Image (b)Result Image
mdb213.jpg(a)Main Image (b)Pectoral Muscle
mdb214.jpg
Main Image
Result Image
o Fat t y t i s s ue are ao Duc to Lobul e so Si nuso l i gam e nt s
Extraction of ROIRemoving pectoral muscle
Why removing pectoral muscle?
o Pectoral muscle will never contain micro-calcification
o Less Computational Time And Cost-Operation on small image area
Existence of micro-calcification:
ROI
Edge Detection of pectoral muscleRemoving pectoral muscle
Possible Approach To Edge-detection:
1.Scanning pixel value intensity at each points2.find out the sudden big intensity change at the edge location3.Mark the pixels at edge location4.Estimate a straight line depending on the marked edge points
Approach-01:
Problem faced in Approach-01:-Finding appropriate Thresholding value is an unsupervised method,
which will work on every image -The threshold value must be found in an unsupervised manner-Any predefined threshold value will not produce desired output for all image
Edge Detection of pectoral muscleRemoving pectoral muscle
Points to be noted from approach-2:
-Pectoral muscle a Triangular areamdb212.jpg
mdb214.jpg
Based on this point: Moving on to approach -03
mdb209.jpg
(2)Binary Image(1)Original Image
Triangle Detection of pectoral muscleRemoving pectoral muscle
1.Fing the triangular area of the pectoral muscle region
I. Finding white seeding pointII. Finding the 1st black point of 1st row after getting a white seeding pointIII. Draw a horizontal line in these two points.IV. finding the 1st black point of 1st column after getting a white seeding pointV. Draw a vertical line and angular line.
2.Making all the pixels black(zero)resides in the pectoral muscle area
Approach-03(Triangle Detection of pectoral muscle):
Visualization in next slide
Triangle Detection of pectoral muscleRemoving pectoral muscle
Approach-03(Triangle Detection of pectoral muscle):
Vertical directions are represented by sub-bands 4-7
(5) sub-band 4
(6) sub-band 5
(7) sub-band 6
(8) sub-band 7
Why Contourlet?
Why Contourlet?
•Decompose the mammographic image:-Into directional components:
To easily capture the geometry of the image features.
Details in upcoming slides
Target
Details in upcoming slides
• This decomposition offers:
-Multiscale localization(Laplacian Pyramid) and -A high degree of directionality and anisotropy.
Why Contourlet? Usefulness of Contourlet
Directionality:Having basis elements Defined in variety of directions
Anisotrophy:Basis Elements having Different aspect ration
Contourlet Transform Concept
(a)Wavelet(Require a lot of dot for fine resolution)
(b)Contourlet(Requires few different elongated shapes
in a variety of direction following the counter)
3 Different Size of Square Shape brush stroke(Smallest, Medium, Largest) to provide Multiresolution Image
Example: Painter Scenario
Why Contourlet?
2-D Contourlet Transform (2D-CT) Discrete WT
Handles singularities such as edges in a more powerful way
Has basis functions at many orientations has basis functions at three orientations
Basis functions appear a several aspectratios
the aspect ratio of DWT is 1
CT similar as DWT can beimplemented using iterative filter banks.
Advantage of using 2D-CT over DWT:
Details in upcoming slides
Input image
BandpassDirectionalsubbands
BandpassDirectionalsubbands
Plan-of-Action
For microcalcifications enhancement :
We use-The Contourlet Transform(CT) [12]
The Prewitt Filter.
12. Da Cunha A. L., Zhou J. and Do M. N,: The Nonsubsampled Contourlet Transform: Theory, Design, and Applications, IEEE Transactions on Image Processing,vol. 15, (2006) pp. 3089-3101
Art-of-Action
An edge Prewitt filter to enhance the directional structures
in the image.
Contourlet transform allows decomposing the image in
multidirectional and multiscale subbands[21].
21. Laine A.F., Schuler S., Fan J., Huda W.: Mammographic feature enhancement by multiscale analysis, IEEE Transactions on Medical Imaging, 1994, vol. 13, no. 4,(1994) pp. 7250-7260
This allows finding • A better set of edges,• Recovering an enhanced mammogram with better visual characteristics.
Microcalcifications have a very small size a denoising stage is not implemented
in order to preserve the integrity of the injuries.
Decompose the digital mammogram
Using Contourlet transform
(b) Enhanced image(mdb238.jpg)
(a) Original image (mdb238.jpg)
Method
CT is implemented in two stages:
1. Subband decomposition stage
2. Directional decomposition stages.
Details in upcoming slides
Method
1. Subband decomposition stage
For the subband decomposition:- The Laplacian pyramid is used [22]
Decomposition at each step:-Generates a sampled low pass version of the original-The difference between :
The original image and the prediction.
22. Park S.-I., Smith M. J. T., and Mersereau R. M.: A new directional Filter bank for image analysis andclassification, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '99), vol.3, (1999) pp. 1417-1420
Details ……..
Method
1. Subband decomposition stage
Details ……..
1. The input image is first low pass filtered
2. Filtered image is then decimated to get a coarse(rough) approximation.
3. The resulting image is interpolated and passed through Synthesis filter.
4. The obtained image is subtracted from the original image :To get a bandpass image.
5. The process is then iterated on the coarser version (high resolution)of the image.
Plan of Action
Method
2.Directional Filter Bank (DFB)
Details ……..
Implemented by using an L-level binary tree decomposition :resulting in 2L subbands
The desired frequency partitioning is obtained by :Following a tree expanding rule
- For finer directional subbands [22].
22. Park S.-I., Smith M. J. T., and Mersereau R. M.: A new directional Filter bank for image analysis andclassification, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '99), vol.3, (1999) pp. 1417-1420
The Contourlet Transform
The CT is implemented by:Laplacian pyramid followed by directional filter banks (Fig-01)
Input image
BandpassDirectionalsubbands
BandpassDirectionalsubbands
Figure 01: Structure of the Laplacian pyramid together with the directional filter bank
The concept of wavelet:University of Heidelburg
The CASCADE STRUCTURE allows:- The multiscale and
directional decomposition to be independent
- Makes possible to:Decompose each scale into
any arbitrary power of two's number of directions(4,8,16…)
Figure 01
Details ………….
Decomposes The Image Into Several Directional Subbands And Multiple Scales
Figure 02: (a)Structure of the Laplacian pyramid together with the directional filter bank(b) frequency partitioning by the contourlet transform(c) Decomposition levels and directions.
(a) (b)
Input image
BandpassDirectionalsubbands
BandpassDirectionalsubbands
Details….
(c)
DenoteEach subband by yi,j
Wherei =decomposition level and J=direction
The Contourlet TransformDecomposes The Image Into Several Directional Subbands And Multiple Scales
The processing of an image consists on:-Applying a function to enhance the regions of
interest.
In multiscale analysis:
Calculating function f for each subband :-To emphasize the features of interest-In order to get a new set y' of enhanced subbands:
Each of the resulting enhanced subbands can be expressed using equation 1.
)(', , jiyfjiy = ………………..(1)
-After the enhanced subbands are obtained, the inverse transform is performed to obtain an enhanced image.
Enhancement of the Directional Subbands
The Contourlet Transform
Denote
Each subband by yi,jWherei =decomposition level and J=direction Details….
Enhancement of the Directional Subbands
The Contourlet Transform
Details….
The directional subbands are enhanced using equation 2.
=)( , jiyf)2,1(
,1 nnWjiy
)2,1(,2 nnWjiy
If bi,j(n1,n2)=0
If bi,j(n1,n2)=1………..(2)
Denote
Each subband by yi,jWherei =decomposition level and J=direction
W1= weight factors for detecting the surrounding tissueW2= weight factors for detecting microcalcifications
(n1,n2) are the spatial coordinates.
bi;j = a binary image containing the edges of the subband
Weight and threshold selection techniques are presented on upcoming slides
Enhancement of the Directional Subbands
The Contourlet Transform
The directional subbands are enhanced using equation 2.
=)( , jiyf)2,1(
,1 nnWjiy
)2,1(,2 nnWjiy
If bi,j(n1,n2)=0
If bi,j(n1,n2)=1………..(2)
Binary edge image bi,j is obtained :-by applying an operator (prewitt edge detector)
-to detect edges on each directional subband.
In order to obtain a binary image:A threshold Ti,j for each subband is calculated.
Details….
Weight and threshold selection techniques are presented on upcoming slides
Threshold Selection
The Contourlet Transform
Details….
The microcalcifications appear :
On each subband Over a very
homogeneous background.
Most of the transform coefficients:
-The coefficients corresponding to theinjuries are far from background value.
A conservative threshold of 3σi;j is selected:where σi;j is the standard deviation of the corresponding subband y I,j .
Weight Selection
The Contourlet Transform
Exhaustive tests:-Consist on evaluating subjectively a set of 322 different mammograms
-With Different combinations of values,
The weights W1, and W2 are determined:-Selected as W1 = 3 σi;j and W2 = 4 σi;j
These weights are chosen to:keep the relationship W1 < W2:
-Because the W factor is a gain -More gain at the edges are wanted.
Experimental Results
Applying Contourlet Transformation Benign
Original image Enhanced image
Goal: Microcalcification Enhancement
mdb222.jpg
mdb223.jpg
Original image Enhanced image
mdb248.jpg
mdb252.jpg
Applying Contourlet Transformation Benign
Original image Enhanced image
mdb226.jpg
mdb227.jpg
Original image Enhanced image
mdb236.jpg
mdb240.jpg
Goal: Microcalcification Enhancement
Applying Contourlet Transformation Benign
Original image Enhanced image Original image Enhanced image
Original image(with diagonal details areas indicated)
Diagonal Details
Use Separable Transform
2D Wavelet Transform
Vertical Details
Decomposition at Label 4
Original image(with Vertical details areas indicated)
Experimental Results
Experimental Results
DWT
1.Original Image(Malignent_mdb238) 2.Decomposition at Label 4
2.Decomposition at Label 1 3.Decomposition at Label 2 3.Decomposition at Label 3
Experimental Results
DWT
1.Original Image(Malignent_mdb238) 2.Decomposition at Label 4
Experimental Results
1.Original Image(Benign_mdb252) 2.Decomposition at Label 4
2.Decomposition at Label 1 3.Decomposition at Label 2 3.Decomposition at Label 3
DWT
Experimental Results
1.Original Image(Malignent_mdb253.jpg) 2.Decomposition at Label 4
2.Decomposition at Label 1 3.Decomposition at Label 2 3.Decomposition at Label 3
Metrics: Quantitive Measurement
Metrics
To compare the ability of :Enhancement achieved by the proposed method
Why?
1. Measurement of distributed separation (MDS)2. Contrast enhancement of background against target (CEBT) and3. Entropy-based contrast enhancement of background against target (ECEBT) [23].
Measures used to compare:
23. Sameer S. and Keit B.: An Evaluation on Contrast Enhancement Techniques for Mammographic Breast Masses, IEEETransactions on Information Technology in Biomedicine, vol. 9, (2005) pp. 109-119
Metrics
1. Measurement of Distributed Separation (MDS)
Measures used to compare:
The MDS represents :How separated are the distributions of each mammogram
Feature ExtractionSURF point algorithmSpeeded-Up Robust Features (SURF) algorithm to find blob features.
I. Finding Key pointsII. Matching key pointsIII. Classification
Estimate Geometric Transformation and Eliminate Outliers
Context in using the features:
Feature ExtractionSURF point algorithmSpeeded-Up Robust Features (SURF) algorithm to find blob features.
I. Finding Key pointsII. Matching key pointsIII. Classification
Feature ExtractionSURF point algorithmSpeeded-Up Robust Features (SURF) algorithm to find blob features.
% Estimate Geometric Transformation and Eliminate Outliers% estimateGeometricTransform calculates the transformation relating the matched points, % while eliminating outliers. This transformation allows us to localize the object in the scene[tform, inlierElephantPoints, inlierScenePoints] = ...
estimateGeometricTransform(matchedElephantPoints, matchedScenePoints, 'affine');figure;% Display the matching point pairs with the outliers removedshowMatchedFeatures(elephantImage, sceneImage, inlierElephantPoints, ...
[2]D.Narain Ponraj, M.Evangelin Jenifer, P. Poongodi, J.Samuel Manoharan “A Survey on the Preprocessing Techniques of Mammogram for the Detection of Breast Cancer”, Journal ofEmerging Trends in Computing and Information Sciences, Volume 2, Issue 12, pp. 656-664,2011
[1]K. Santle Camilus , V. K. Govindan, P.S. Sathidevi,” Pectoral muscle identification inmammograms”, Journal of Applied Clinical Medical Physics , Vol. 12 , Issue No. 3 , 2011
[3]Arnau Oliver, Albert Torrent, Meritxell Tortajada, Xavier Llad´o,Marta Peracaula,Lidia Tortajada, Melcior Sent´ıs, and Jordi Freixenet,” Automatic microcalcification and clusterdetection for digital and digitised mammograms”, Springer-Verlag Berlin Heidelberg, 36,pp. 251–258, 2010
Reference
[4]Arnau Olivera, Albert Torrenta , Xavier Lladóa , Meritxell Tortajada, Lidia Tortajadab,Melcior Sentísb, Jordi Freixeneta, Reyer Zwiggelaarc,” Automatic microcalcification and clusterdetection for digital and digitised mammograms”,Elsevier:Knowledge-Based Systems,Volume 28, pp. 68–75, April 2012.
[5]A.Papadopoulos, D.I . Fotiadis, L.Costrrido,” Improvement of microcalcification clusterdetection in mammogaphy utilizing image enhancement techniques”.Comput.Bio.Med.10,Vol 38,Issue 38,pp.1045-1055,2008
[6]N.R.Pal,B.Bhowmik, S.K.Patel, S.Pal, J.Das,”A multi-stage nural network aided system fordetection of microcalcification in digitized mammogeams”,Neurocomputing, Vol 11,pp.2625-2634,2008
[7]M.Rizzi, M.D’Aloia, B.Castagnolo,” Computer aided detection of microcalcification in digitalMammograms adopting a wavelet decomposition ”,Integr.Comput.-Aided Eng.,Vol 16,Issue 2,pp.91-103,2009
Reference
[8]S.N.Yu, Y.K. Huang,” Detection of microcalcifications on digital mammograms using combinedModel-based and statistical textural features”, Expert Syst.Appl. , Vol 37,Issue 7,pp.5461-5469,2010
[9]Wang T. C and Karayiannis N. B.: Detection of Microcalci¯cations in Digital Mammograms Using Wavelets, IEEE Transaction on Medical Imaging, vol. 17, no. 4,(1989) pp. 498-509
[10]. Daubechies I.: Ten Lectures on Wavelets, Philadelphia, PA, SIAM, (1992)
[11] Strickland R.N. and Hahn H.: Wavelet transforms for detecting microcalcificationsin mammograms, IEEE Transactions on Medical Imaging, vol. 15, (1996) pp. 218-229
[12]Heinlein P., Drexl J. and Schneider Wilfried: Integrated Wavelets for Enhancement of Microcalcifications in Digital Mammography, IEEE Transactions on Medical Imaging, Vol. 22, (2003) pp. 402-413
[13]. Zhibo Lu, Tianzi Jiang, Guoen Hu, Xin Wang: Contourlet based mammographicimage enhancement, Proc. of SPIE, vol. 6534, (2007) pp. 65340M-1 - 65340M-8
Reference
[14]Fatemeh Moayedi, Zohreh Azimifar, Reza Boostani, and Serajodin Katebi: Contourlet-based mammography mass classification, ICIAR 2007, LNCS 4633,(2007) pp. 923-934
[15] Balakumaran T., Vennila ILA, Shankar C.G: Detection of Microcalcification in Mammograms Using Wavelet Transform and Fuzzy Shell Clustering, International Journal of Computer Science and Information Security, Vol 7,Issue 1,pp.121-125,2010
[16] Zhang X., Homma N., Goto S.,Kawasumi Y., Ihibashi T.,Abe M.,Sugita N.,Yoshizawa M: A Hybrid Image Filtering Method for Computer-Aided Detection of Microcalcification Clusters in Mammograms, Journal of Medical Engineering, Vol 3,Issue 1,pp.111-119,2013
[17] Lu J., Ikehara T., Zhang Y,Mihara T., Itoh T.,Maeda R:High quality factor silicon cantilever driven by piezoelectric thin film actuator for resonant based mass detection, Micro system Technologies , Vol 15, Issue 8, pp:1163-1169., 2009
[19] Shankla V, David D. P, Susan P. Weinstein; Michael D., Tuite C, Roth R., Emily F:Automatic insertion of simulated microcalcification clusters in a software breast phantom, , Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 2014
[20] Otsu, N., "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.
[21]Laine A.F., Schuler S., Fan J., Huda W.: Mammographic feature enhancement by multiscale analysis, IEEE Transactions on Medical Imaging, 1994, vol. 13, no. 4,(1994) pp. 7250-7260
22. Park S.-I., Smith M. J. T., and Mersereau R. M.: A new directional Filter bank forimage analysis and classification, Proceedings of IEEE International Conference onAcoustics, Speech, and Signal Processing (ICASSP '99), vol. 3, (1999) pp. 1417-1420
23. Sameer S. and Keit B.: An Evaluation on Contrast Enhancement Techniques forMammographic Breast Masses, IEEE Transactions on Information Technology inBiomedicine, vol. 9, (2005) pp. 109-119
Reference
24. Rosten, E., and T. Drummond. "Machine Learning for High-Speed Corner Detection." 9th European Conference on Computer Vision. Vol. 1, 2006, pp. 430–443.
25. Shi, J., and C. Tomasi. "Good Features to Track." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. June 1994, pp. 593–600.
26. Harris, C., and M. J. Stephens. "A Combined Corner and Edge Detector." Proceedings of the 4th Alvey Vision Conference. August 1988, pp. 147–152.
27 Bay, H., A. Ess, T. Tuytelaars, and L. Van Gool. "SURF: Speeded Up Robust Features." Computer Vision and Image Understanding (CVIU). Vol. 110, No. 3, 2008, pp. 346–359.
28.Leutenegger, S., M. Chli, and R. Siegwart. "BRISK: Binary Robust Invariant Scalable
29.Matas, J., O. Chum, M. Urba, and T. Pajdla. "Robust wide-baseline stereo from maximally stable extremal regions."Proceedings of British Machine Vision Conference. 2002, pp. 384–396.
Reference
30. Oliver A.; Torrent A. , Tortajada M, Liado X, R., Preacaula M , Tortajada L., Srntis M.,Ferixenet J: A Boosting based approach for automatic Microcalcification Detection,Springer-Verlag Berlin Heldelberg,Lecture notes on Computer Science (LNCS 6136), (2010)pp. 251- 258