Art. 06 – Vol. 21 – No. 2 – 2011

Advanced Technologies in Processing Biomedical Images Using Shape Recognition Algorithms

Case Study: Liver Disorders

Dragoş Nicolau
dragos@ici.ro
Dragoş Barbu
dbarbu@ici.ro
Laura Ciocoiu
ciocoiu@ici.ro
Dragoş Smada
dsmada@ici.ro
National Institute for Research & Development of Informatics – ICI Bucharest

Abstract: This paper presents an interactive system based on algorithms for analysis, segmentation and recognition of organs obtained from magnetic resonance images (MRI), ultrasounds (US) or computed tomography (CT). The purpose of image segmentation is to group clusters of pixels in the continuous regions, for example, regions corresponding to individual surfaces, natural objects or parts of objects. Segmentation is used for recognition bodies, viewed as objects, estimating the limits of encounter between organs or systems în case of motion or stereo systems of images compression, image editing, or the search for images în databases.

Keywords: analysis, medical image, image segmentation, compression, reconstruction

View full article

BREFERENCES:

Pratt, William K. Digital image processing: PIKS Scientific inside. 4th ed., A Wiley-Interscience publication, ISBN: 978-0-471-76777-0, 2007.

JAIN, ANIL K. Fundamentals of digital image processing. Prentice Hall Information and System Sciences Series, ISBN: 0-13-336165-9, 1989.
Bankmann, Isaac N. Handbook of Medical Imaging. Processing and Analysis. Academic Press Series în Biomedical Engineering, ISBN: 0-12-077790-8, October 2000.
Handbook of Biomedical Image Analysis. Vol. 1: Segmentation Models Part A. Eds.: D. L. W. Jasjit Suri, Swamy Laximinarayan Kluwer Academic/ Plenum Publishers, 2005.
Handbook of Biomedical Image Analysis. Vol. 2: Segmentation Models Part B. Eds.: D. L. W. Jasjit Suri, Swamy Laximinarayan Kluwer Academic/ Plenum Publishers, ISBN:0-306-48605-9, 2005.
LEE, CHRISTINA W. C.; M. E. TUBLIN; B. E. CHAPMAN. Registration of MR and CT Images of the Liver: Comparison of Voxel Similarity and Surface Based Registration Algorithms. Comput Methods Programs Biomed, 2005, May; 78(2):101-14.
MARTIN-FERNANDEZ, M.; ALBEROLA-LOPEZ, C. An Approach for Contour Detection of Human Kidneys from Ultrasound Images using Markov Random Fields and Active Contours. Medical Image Analysis, 2005.
MASUTANI, Y.; K. UOZUMI; AKAHANE MASAAKI; OHTOMO KUNI. Liver CT Image Processing: a Short Introduction of the Technical Elements. European journal of radiology, 2006; 58(2):246-51.
FASQUEL, J.-B.; V. AGNUS; J. MOREAU; L. SOLER; J. MARESCAUX. An Interactive Medical Image Segmentation System Based on the Optimal Management of Regions of Interest Using Topological Medical Knowledge. Computer methods and programs în biomedicine, 82 (3) 216–230, 2006.
SEONG-JAE LIM; YONG-YEON JEONG; YO-SUNG HO. Automatic Liver Segmentation for Volume Measurement în CT Images. J. Visual Communication and Image Representation, 17(4): 860-875 (2006).
STOITSIS, JOHN; IOANNIS VALAVANIS; STAVROULA G. MOUGIAKAKOU; SPYRETTA GOLEMATI; ALEXANDRA NIKITA; KONSTANTINA S. NIKITA. Computer Aided Diagnosis Based on Medical Image Processing and Artificial Intelligence Methods. Nuclear Instruments and Methods în Physics Research Section A, Volume 569, Issue 2, pp. 591-595.
CHIA-HSIANG, WU; SUN YUNG-NIEN. Segmentation of Kidney from Ultrasound B-mode Images with Texture-Based Classification. Computer Methods and Programs în Biomedicine, 84(2-3): 114-123 (2006).
CAMPADELLI, PAOLA; ELENA CASIRAGHI; ANDREA ESPOSITO. Liver Segmentation from Computed Tomography Scans: A Survey and a New Algorithm. Artificial Intelligence în Medicine, 45(2-3): 185-196 (2009).
BEZY-WENDLING, JOHANNE; KRETOWSKI MAREK. Physiological modeling of tumor-affected renal circulation. Computer Methods and Programs în Biomedicine, 91(1): 1-12 (2008).
ZAFER, ISCAN; YÜKSEL AYHAN; DOKUR ZÜMRAY; KORÜREK MEHMET; ÖLMEZ TAMER. Medical Image Segmentation with Transform and Moment Based Features and Incremental Supervised Neural Network. Digital Signal Processing, Vol. 19, Issue 5, September 2009, pp. 890-901.
SPIEGEL, MARTIN; HAHN DIETER A.; DAUM VOLKER; WASZA JAKOB; HORNEGGER JOACHIM. Segmentation of Kidneys Using a new Active Shape Model Generation Technique Based on Non-Rigid Image Registration. Computerized Medical Imaging and Graphics, 33 (2009), No. 1, pp. 29-39.
Takayuki, Kitasaka; Mori Kensaku; Suenaga Yasuhito. Development of Advanced Image Processing Technology and Its Application to Computer Assisted Diagnosis and Surgery. K. Aizawa, Y. Nakamura, and S. Satoh (Eds.): Pacific Rim Conference on Multimedia 2004, LNCS 3331, Springer-Verlag Berlin Heidelberg, 2004, pp. 514–521.
WALA, TOUHAMI; BOUKERROUI DJAMAL; COCQUEREZ JEAN-PIERRE. Fully Automatic Kidneys Detection în 2D CT Images: A Statistical Approach. J. Duncan and G. Gerig (Eds.): MICCAI 2005, LNCS 3749, Springer-Verlag Berlin Heidelberg 2005 pp. 262–270.
WONG, KOON-PONG. Medical Image Segmentation: Methods and Applications în Functional Imaging. Springer, ISBN: 0-306- 48605-9; Handbook of Biomedical Image Analysis, Vol.2: Segmentation Models, Part B, Chapter 3, 2005, pp. 111-182.
BOMMANNA RAJA, K.; M. MADHESWARAN; K. THYAGARAJAH. Ultrasound Kidney Image Analysis for Computerized Disorder Identification and Classification Using Content Descriptive Power Spectral Features. Journal of Medical Systems, Vol. 31, 2007, pp. 307–317.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.