000 04469nam a22005175i 4500
001 978-3-031-01654-7
003 DE-He213
005 20240730164351.0
007 cr nn 008mamaa
008 220601s2012 sz | s |||| 0|eng d
020 _a9783031016547
_9978-3-031-01654-7
024 7 _a10.1007/978-3-031-01654-7
_2doi
050 4 _aT1-995
072 7 _aTBC
_2bicssc
072 7 _aTEC000000
_2bisacsh
072 7 _aTBC
_2thema
082 0 4 _a620
_223
100 1 _aCabral, Thanh M.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_984124
245 1 0 _aFractal Analysis of Breast Masses in Mammograms
_h[electronic resource] /
_cby Thanh M. Cabral, Rangaraj M. Rangayyan.
250 _a1st ed. 2012.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2012.
300 _aXVI, 104 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Biomedical Engineering,
_x1930-0336
505 0 _aComputer-Aided Diagnosis of Breast Cancer -- Detection and Analysis of\newline Breast Masses -- Datasets of Images of Breast Masses -- Methods for Fractal Analysis -- Pattern Classification -- Results of Classification of Breast Masses -- Concluding Remarks.
520 _aFractal analysis is useful in digital image processing for the characterization of shape roughness and gray-scale texture or complexity. Breast masses present shape and gray-scale characteristics in mammograms that vary between benign masses and malignant tumors. This book demonstrates the use of fractal analysis to classify breast masses as benign masses or malignant tumors based on the irregularity exhibited in their contours and the gray-scale variability exhibited in their mammographic images. A few different approaches are described to estimate the fractal dimension (FD) of the contour of a mass, including the ruler method, box-counting method, and the power spectral analysis (PSA) method. Procedures are also described for the estimation of the FD of the gray-scale image of a mass using the blanket method and the PSA method. To facilitate comparative analysis of FD as a feature for pattern classification of breast masses, several other shape features and texture measures are described in the book. The shape features described include compactness, spiculation index, fractional concavity, and Fourier factor. The texture measures described are statistical measures derived from the gray-level cooccurrence matrix of the given image. Texture measures reveal properties about the spatial distribution of the gray levels in the given image; therefore, the performance of texture measures may be dependent on the resolution of the image. For this reason, an analysis of the effect of spatial resolution or pixel size on texture measures in the classification of breast masses is presented in the book. The results demonstrated in the book indicate that fractal analysis is more suitable for characterization of the shape than the gray-level variations of breast masses, with area under the receiver operating characteristics of up to 0.93 with a dataset of 111 mammographic images of masses. The methods and results presented in the book are useful for computer-aided diagnosis of breast cancer. Table of Contents: Computer-Aided Diagnosis of Breast Cancer / Detection and Analysis of\newline Breast Masses / Datasets of Images of Breast Masses / Methods for Fractal Analysis / Pattern Classification / Results of Classification of Breast Masses / Concluding Remarks.
650 0 _aEngineering.
_99405
650 0 _aBiophysics.
_94093
650 0 _aBiomedical engineering.
_93292
650 1 4 _aTechnology and Engineering.
_984126
650 2 4 _aBiophysics.
_94093
650 2 4 _aBiomedical Engineering and Bioengineering.
_931842
700 1 _aRangayyan, Rangaraj M.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_984129
710 2 _aSpringerLink (Online service)
_984132
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031005268
776 0 8 _iPrinted edition:
_z9783031027826
830 0 _aSynthesis Lectures on Biomedical Engineering,
_x1930-0336
_984133
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01654-7
912 _aZDB-2-SXSC
942 _cEBK
999 _c85620
_d85620