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DIGITAL SIGNAL PROCESSING IN ANALYSIS OF BIOMEDICAL IMAGES

DIGITAL SIGNAL PROCESSING IN ANALYSIS OF BIOMEDICAL IMAGES. Prof. Aleš Procházka Institute of Chemical Technology in Prague Department of Computing and Control Engineering Digital Signal and Image Processing Research Group. 1. INTRODUCTION. MOTIVATION OF THE DSP RESEARCH GROUP

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DIGITAL SIGNAL PROCESSING IN ANALYSIS OF BIOMEDICAL IMAGES

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  1. DIGITAL SIGNAL PROCESSINGIN ANALYSIS OF BIOMEDICAL IMAGES Prof. Aleš Procházka Institute of Chemical Technology in Prague Department of Computing and Control Engineering Digital Signal and Image Processing Research Group

  2. 1. INTRODUCTION • MOTIVATION OF THE DSP RESEARCH GROUP • INTEGRATION ROLE OF SIGNAL • AND IMAGE PROCESSING IN • THE FRAME OF INFORMATION • ENGINEERING • Interdisciplinary area connecting mathematics and engineering: control, measuring engineering, vision, speech processing, biomedicine, environmental engineering … • Fundament for data acquisition, system identification and modelling, signal de-noising, feature extraction, segmentation, classification, compression, prediction, … • Similar mathematical background based on methods of time-frequency and time-scale analysis in different areas

  3. 2. APPLICATIONS INTERESTS OF DSP RESEARCH GROUP Signal Prediction Environmental Engineering Biomedical Image Analysis Remote Data Processing

  4. 3. TIME-FREQUENCY ANALYSIS DISCRETE FOURIER TRANSFORM IN RESOLUTION ENHANCEMENT 2-D DFT for k=0,1,…,N/2 – 1, l= 0,1,…,M/2 – 1 and f1(k)=k/N , f2(l)=l/M 1-D DFT fork=0,1,…,N/2 – 1 and f(k)=k/N

  5. 4. TIME-SCALE ANALYSIS WAVELET TRANSFORM IN SIGNAL PARTS DETECTION • Initial wavelet defined either in the analytical form or by a dilation equation • Dilation and translation coefficients: a=2^m, b=k 2^m • Initial wavelet is a pass-band filter • Wavelet dilation corresponds to its pass-band compression

  6. 5. DENOISING OF SIGNAL / IMAGE COMPONENTS WAVELET TRANSFORM IN IMAGE DENOISING • ALGORITHM • Decomposition stage: – convolution of a given signal and the filter • – downsampling by D • Coefficients - by rows and columns thresholding Magneticresonance image • Reconstruction stage: • – row upsampling by • factor U and • row convolution • – sum of the • corresponding • images • – column upsampling • by factor U and • column convolution

  7. 6. MR IMAGE RESOLUTION ENHANCEMENT WAVELET TRANSFORM IN IMAGE RESOLUTION ENHANCEMENT I. Image Resolution Enhancement using DFT • MAGNETIC RESONANCE • IMAGES OF A HUMAN BRAIN • Original resolution: • 512 x 512 pixels • Resolution enhancement: • 1024 x 1024 pixels II. Image Resolution Enhancement using DWT • CONCLUSIONS • DFT: the structures and edges are very smooth • DWT: sharper edges obtained • DFT and DWT: various methods to enhance the resolution can be applied

  8. 7. IMAGE RESTORATION METHODS OF IMAGE COMPONENTS RESTORATION • METHODS • Detection of featuresof missing regions and their replacement by the • most similar ones • Multidirectional prediction of • missing image • parts • Multidemensional cubic and spline interpolation • Iterated wavelet interpolation

  9. 8. ITERATED WAVELET TRANSFORM IN IMAGE RESTORATION WAVELET TRANSFORM IN ITERATED INTERPOLATION • ALGORITHM • Image decomposition into a selected level • Wavelet coefficients thresholding • Image reconstruction • Replacement of values outside regions of interest by original values • The next iteration of image decomposition

  10. 9. IMAGE SEGMENTATION WATERSHED TRANSFORM IN IMAGE SEGMENTATION • ALGORITHM • Image thresholding and denoising • Distance and watershed transform use • Extraction of individual segments • Analysis of image components boundary signals and texture

  11. 10. FEATURE EXTRACTION AND CLASSIFICATION RADON TRANSFORM IN ROTATION INVARIANT TEXTURE FEATURES ESTIMATION • ALOGORITHM • Radon transform use for conversion of rotation to translation • Translation invariant wavelet transform use for feature estimation • Classification by neural networks

  12. 11. FEATURE BASED SEGMENTATION FEATURE BASED BIOMEDICAL IMAGE SEGMENTATION • PRINCIPLE • Each root pixel of the original image is associated with its feature • derived from its neighbourhood • Pixels are individually classified into selected number of levels

  13. 12. CONCLUSION COLLABORATION • European Association for Signal and Image Processing • IEE London, IEEE • University of Cambridge, Brunel University, UK • University Las Palmas, Spain SELECTED PAPERS • A. Procházka, I. Šindelářová, and J. Ptáček. Image De-noising and Restoration using Wavelet Transform . In European Control Conference ECC 2003 Conference Papers, Cambridge, UK, 2003. • A. Procházka and J. Ptácek. Wavelet Transform Application in Biomedical Image Recovery and Enhancement . In P. of 8th Multi-Conf. Systemics, Cybernetics and Informatic, Orlando, USA, 2004 • A. Procházka, A. Gavlasova, M. Mudrova. Rotation Invariant Biomedical Object Recognition. In Proc. of the EUSIPCO Conf., EURASIP, Italy, 2006

  14. Institute of Chemical Technology in Prague Research Group of Digital Signal and Image Processing http: // dsp.vscht.cz

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