Assessment of Feasibility to Use Computer Aided Texture Analysis Based Tool for Parametric Images of Suspicious Lesions in DCE-MR Mammography

Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Volume 2013, Article ID 872676, 6 pages http://dx.doi.org/10.1155/2013/872676 Research Article Assessment of Feasibility to Use Computer Aided Texture Analysis Based Tool for Parametric Images of Suspicious Lesions in DCE-MR Mammography Mehmet Cemil Kale,1 John David Fleig,2 and NazJm Emal1 1 2 Bilecik Şeyh Edebali University, Turkey Franklin University, OH, USA Correspondence should be addressed to Mehmet Cemil Kale; Received 2 October 2012; Accepted 6 March 2013 Academic Editor: Anke Meyer-Baese Copyright © 2013 Mehmet Cemil Kale et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Our aim was to analyze the feasibility of computer aided malignant tumor detection using the traditional texture analysis applied on two-compartment-based parameter pseudoimages of dynamic contrast-enhanced magnetic resonance (DCE-MR) breast image data. A major contribution of this research will be the through-plane assessment capability. Texture analysis was performed on two-compartment-based pseudo images of DCE-MRI datasets of breast data of eight subjects. The resulting texture parameter pseudo images were inputted to a feedforward neural network classification system which uses the manual segmentations of a primary radiologist as a gold standard, and each voxel was assigned as malignant or nonmalignant. The classification results were compared with the lesions manually segmented by a second radiologist. Results show that the mean true positive fraction (TPF) and false positive fraction (FPF) performance of the classifier vs. primary radiologist is statistically as good as the mean TPF and FPF performance of the second radiologist vs. primary radiologist with a confidence interval of 95% using a one-sample 𝑡-test with 𝛼 = 0.05. In the experiment implemented on all of the eight subjects, all malignant tumors marked by the primary radiologist were classified to be malignant by the computer classifier. Our results have shown that neural network classification using the textural parameters for automated screening of two-compartment-based parameter pseudo images of DCE-MRI as input data can be a supportive tool for the radiologists in the preassessment stage to show the possible cancerous regions and in the postassessment stage to review the segmentations especially in analyzing complex DCE-MRI cases. 1. Introduction Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important imaging approach for the evaluation of microcirculation in tumors [1]. It is becoming a capable noninvasive method to monitor tumor response to therapies; however, current practice requires radiologists to manually identify and segment tumors from the imaging data. Frequently used methods of identifying tumors on DCE-MR images are based on pseudoimages of parameters obtained by locally fitting time-intensity curves to a two compartment exchange model for contrast agent concentration [2] or use the three time point method [3]. These approaches are very tedious, time consuming, and prone to intra- and interobserver variation. Texture analysis has been applied in medical imaging for several applications. Tourassi described the role of image texture analysis in the medical imaging field [4]. Lerski et al. used texture analysis on MRI for tissue characterization and segmentation of brain tissues [5]. Kovalev et al. implemented a method for object recognition and matching with multidimensional cooccurrence matrix calculations [6– 8]. Gibbs and Turnbull used a cooccurrence-based texture tool on postcontrast MR breast images, showing that there is a difference in terms of the spatial variations in voxel intensities between benign and malignant lesions [9]. Neural networks are widely used for image classification [10]. Some examples of neural network classification on MRI prior to our method are briefly presented here. Vergnaghi et al. used an artificial neural network 2 to automatically classify the enhancement curves as benign or malignant [11]. Lucht et al. described a voxel-by-voxel neural network classifier which compared the performance of quantitative methods for the characterization of signal-time curves acquired by DCE-MR mammography [12–14]. Tzacheva et al. implemented a region-based neural network classifier which segments malignant tissues using a static postcontrast T1weighted image as input [15]. Szabó et al. implemented an artificial-neural-network- (ANN-) based segmentation method for DCE-MRI of the breast and compared it with quantitative and empirical parameter mapping techniques to test the discriminative ability of kinetic, morphologic, and combined MR features [16, 17]. Twellmann et al. introduced a model-free neural network classifier where he suggested that an improvement could likely be achieved using texture features [18]. We present a computer aided diagnostic (CAD) tool based on statistical texture voxel-by-voxel analysis of pseudoimages generated by the local curve fitting of parameters from a two compartment exchange model. The texture processing is based on statistical methods originally presented by Haralick et al. [19]. The CAD tool we describe in this paper is a texture-based classifier which uses two-compartmentbased DCE-MRI breast datasets. Compared to the studies of Gibbs and Turnbull, the work reported here focuses on the parameter pseudoimages derived from curve fitting of the two-compartment model from the original MRI dataset. Classification of the outcome images by the texture-based analysis tool is obtained using an artificial neural network trained with data marked by a radiologist. 2. Materials and Methods 2.1. Data Acquisition. The two-compartment model for contrast agent exchange is the basis for the work presented here [20]. In this model, the primary compartment represents intravascular space, with extravascular (extracellular) space as the secondary compartment. Typically the twocompartment model is characterized by the following parameters. The first-order rate constant for transfer from the primary (vascular) compartment to the secondary (extravascular, extracellular) is 𝑘pe . The rate constant in the reverse direction is 𝑘ep . Injection flow rate for the contrast agent is 𝑘in , and the first-order rate constant for elimination of the contrast agent from the primary compartment is 𝑘el . The peak intensity reached for a given voxel during the time course of the DCE-MRI acquisition is 𝐴. To identify voxels representing malignant tissues, radiologists rely on the property that microvessels in malignant tissues are more frequent and porous than normal. This difference is represented in the local parametric values of 𝐴, 𝑘ep , and 𝑘el , which are used by the reader to mark voxels and regions as mali (...truncated)