Fully automatic kidneys detection in 2d CT images: a statistical approach

Authors:
Wala Touhami;Djamal Boukerroui;Jean-Pierre Cocquerez
Affiliations:
HEUDIASYC, UMR CNRS #6599, Université de Technologie de Compiègne, Compiègne, France;HEUDIASYC, UMR CNRS #6599, Université de Technologie de Compiègne, Compiègne, France;HEUDIASYC, UMR CNRS #6599, Université de Technologie de Compiègne, Compiègne, France
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2005

Citing 5
Cited 3

Numerical Recipes in C: The Art of Scientific Computing

Numerical Recipes in C: The Art of Scientific Computing
Segmentation of ultrasound images: multiresolution 2D and 3D algorithm based on global and local statistics

Pattern Recognition Letters - Speciqal issue: Ultrasonic image processing and analysis
Linear-time connected-component labeling based on sequential local operations

Computer Vision and Image Understanding
Image retrieval using mixture models and EM algorithm

SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Identifying multiple abdominal organs from CT image series using a multimodule contextual neural network and spatial fuzzy rules

IEEE Transactions on Information Technology in Biomedicine

Distributed computation of feature-detectors for medical image processing on GPGPU and cell processors

Euro-Par 2010 Proceedings of the 2010 conference on Parallel processing
Modeling of intensity priors for knowledge-based level set algorithm in calvarial tumors segmentation

MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
High-Throughput-Screening of medical image data on heterogeneous clusters

LSSC'11 Proceedings of the 8th international conference on Large-Scale Scientific Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we focus on automatic kidneys detection in 2D abdominal computed tomography (CT) images. Identifying abdominal organs is one of the essential steps for visualization and for providing assistance in teaching, clinical training and diagnosis. It is also a key step in medical image retrieval application. However, due to gray levels similarities of adjacent organs, contrast media effect and relatively high variation of organ’s positions and shapes, automatically identifying abdominal organs has always been a challenging task. In this paper, we present an original method, in a statistical framework, for fully automatic kidneys detection. It makes use of spatial and gray-levels prior models built using a set of training images. The method is tested on over 400 clinically acquired images and very promising results are obtained.