Segmentation and grading of brain tumors on apparent diffusion coefficient images using self-organizing maps

https://doi.org/10.1016/j.compmedimag.2007.04.004Get rights and content

Abstract

An accurate computer-assisted method to perform segmentation of brain tumor on apparent diffusion coefficient (ADC) images and evaluate its grade (malignancy state) has been designed using a mixture of unsupervised artificial neural networks (ANN) and hierarchical multiresolution wavelet. Firstly, the ADC images are decomposed by multiresolution wavelets, which are subsequently selectively reconstructed to form wavelet filtered images. These wavelet filtered images along with FLAIR and T2 weighted images have been utilized as the features to unsupervised neural network – self organizing maps (SOM) – to segment the tumor, edema, necrosis, CSF and normal tissue and grade the malignant state of the tumor. A novel segmentation algorithm based on the number of hits experienced by Best Matching Units (BMU) on SOM maps is proposed. The results shows that the SOM performs well in differentiating the tumor, edema, necrosis, CSF and normal tissue pattern vectors on ADC images. Using the trained SOM and proposed segmentation algorithm, we are able to identify high or low grade tumor, edema, necrosis, CSF and normal tissue. The results are validated against manually segmented images and sensitivity and the specificity are observed to be 0.86 and 0.93, respectively.

Introduction

The diagnosis of brain tumors is an important application for various medical imaging techniques. Magnetic resonance imaging is the preferred technique for detecting and characterizing brain tumors in medical imaging. Correct identification of tumor grade decides the subsequent treatment.

Several approaches have been proposed to effectively segment and grade brain tumors. Fletcher-heath et al. [1] proposed an automatic segmentation technique which separates non enhancing brain tumor using an unsupervised fuzzy clustering method. Clark et al. [2], [3] reported a knowledge-based technique for brain tumor segmentation. In their method, conventional MR images (T1-weighted, proton density, and T2-weighted) were utilized in a system that integrated knowledge-based techniques with multispectral analysis. Liu et al. [4] proposed a system based on fuzzy connectedness method for quantifying high grade tumor such as glioblastoma. In this method, T1 weighted images with Gadolinium enhancement have been utilized to gather information about different aspects of the tumor and its vicinity. Time and Axel [5] reported a novel method to evaluate the malignancy state of breast tumors by an unsupervised method – self organizing maps – using the Dynamic contrast enhanced magnetic resonance imaging. SOM technique has been extensively utilized to handle extremely complex data and cluster them but very little attention has been given to utilizing it for segmentation. Reddick et al. [6] developed a pixel-based two-stage approach where a SOM is trained to segment multispectral MR images which are subsequently classified into white matter, gray matter, etc., by a feed-forward ANN. Hierarchical network architecture has been developed for optical character recognition [7] and for segmentation of range images [8].

In spite of the above cited activities in research, a unique technique to segment and grade brain tumors has not yet been reported. In this study, a method based on SOM has been proposed to segment and grade brain tumors using magnetic resonance images. The reason for choosing the unsupervised technique – SOM – for this work is its ability to cluster extremely complex data [9]. It creates a set of prototype vectors representing the data set and carries out a topology preserving projection of the prototypes from the high dimensional input space onto a low-dimensional grid. This ordered grid can be used as a convenient visualization surface for showing different patterns of the SOM and thus of the data.

Conventionally, magnetic resonance images such as T1, T2 and FLAIR weighted images have been used in evaluating brain tumors. Nevertheless, evaluation of the tumors solely on the basis of T1, T2 and FLAIR weighted images is not successful in every case [10], [11], [12]. Recent advances in medical imaging suggest that values obtained from apparent diffusion coefficient (ADC) maps of diffusion-weighted images may play a role in the evaluation of tumors [13], [14], [15]. Diffusion-weighted magnetic resonance imaging provides image contrast through measurement of the diffusion properties of water within tissues. Application of diffusion sensitizing gradients to the MR pulse sequence allows water molecular displacement over distances of 1–20 μm to be recognized. Combining images obtained with different amounts of diffusion weighting provides an apparent diffusion coefficient (ADC) map. In brain tumor imaging, ADC maps have been successfully used to distinguish brain tumors from oedema. They are also increasingly exploited to differentiate low grade and high grade region where increased cellularity of high grade lesions restricts water motion in a reduced extracellular space. Thus, characterization of tumors by ADC maps may not only help to differentiate tumors based on cellularity, but also to demarcate tumors from surrounding tissue and to grade the malignancy.

In this study, we sought to determine whether SOM is capable of segmenting and grading brain tumors using the information provided by ADC maps, wavelet filtered images of ADC maps, along with conventional images such as FLAIR and T2 weighted images. The cellularity of tissues represented in ADC maps is resolved by employing a multiresolution wavelet technique. The wavelet is a promising technique for medical image segmentation task [16], [17], [18] due to its ability to provide information at the different resolution levels. The multiresolution approach of wavelet has been identified as the ideal tool for extracting local textural features from the images [19], [20].

Section snippets

Data acquisition

Using a 1.5-T MR unit, we obtained axial T2 weighted images with imaging parameters of 4200/99 (TR/TE), a slice thickness of 5 mm, FLAIR images with imaging parameters of 8200/114 (TR/TE), a slice thickness of 5 mm and DWIs with imaging parameters of 3800/107 (TR/TE), a slice thickness of 5 mm. The DWIs were acquired with b values of 0, 1000 in three perpendicular directions by using the echo-planar imaging (EPI). Number of slices acquired in every protocol was six and number of averages employed

Results and discussions

To qualitatively evaluate the segmentation result, several measurements has been applied which are based on Receiver Operating Characteristics (ROC) analysis [30] and the similarity of segmented regions. Let GT denote the segmented volume provided as ground truth, that is the manually segmented regions, GTc its complement and Seg the segmented region obtained by the self-organizing map approach. The considered measurements are:

  • The measures inspired by ROC analysis:

    • The True Positive Fraction

Conclusion

Self-organizing map is an unsupervised competitive neural network that uses the neighborhood interaction set to approximate lateral neural interaction and discovers the topological structure hidden in the pattern vector for visual display in one or two dimensional space. The results of our study shows that the self organizing maps might be used to segment tumor, necrosis, cysts, and edema, and normal tissue and grade the tumors simultaneously on the ADC images, its wavelet filtered images along

C. Vijayakumar was born in Coimbatore, India, in 1979. He received the “Masters in Physics” degree from Bharathiyar University, Coimbatore, India in 2001. In 2002, he joined Defense Research and Development organization (DRDO) as Scientist and currently working in Department of Radiodiagnosis and Imaging, Armed Forces Medical College, Pune, India. His research interests are artificial neural networks, image segmentation, image vision, multiresolution analysis.

References (32)

  • J. Vesanto et al.

    Clustering of the self-organizing map

    IEEE Trans Neural Netw

    (2000)
  • E.M. Stephan et al.

    Normal brain and brain tumor: multicomponent apparent diffusion coefficient line scan imaging

    Radiology

    (2001)
  • A.N. Hasso et al.

    Supratentorial neoplasms

  • R.J. Hicks

    Supratentorial brain tumors

  • B. Nail et al.

    Combination of single-voxel proton MR spectroscopy and apparent diffusion coefficient calculation in the evaluation of common brain tumors

    AJNR

    (2003)
  • Y. Fumiyuki et al.

    Apparent diffusion coefficient of human brain tumors at MR imaging

    Radiology

    (2005)
  • Cited by (71)

    • Radiomics for peripheral zone and intra-prostatic urethra segmentation in MR imaging

      2019, Biomedical Signal Processing and Control
      Citation Excerpt :

      Neurons are arranged on a flat grid and there is no hidden layer in between input layer and output layer. SOMs depend upon a topological map strongly relying on physiological inspiration [54]. PZ and intra-prostatic urethra segmentation

    • Deep learning for automated brain tumor segmentation in MRI images

      2018, Soft Computing Based Medical Image Analysis
    • MiMSeg - an algorithm for automated detection of tumor tissue on NMR apparent diffusion coefficient maps.

      2017, Information Sciences
      Citation Excerpt :

      Exemplary segmented DWIs are presented on Fig. 10. The results of MiMSeg algorithm were compared to few published methods: a neural network based technique named Self-Organizing-Map (SOM) [39] methods proposed by Murakami et al. [40], and by Kang et al. [41]. Although all of these methods have been designed to distinguish among tumors with different grade, they can be used as simple healthy tissue versus IV grade tumor classifiers.

    View all citing articles on Scopus

    C. Vijayakumar was born in Coimbatore, India, in 1979. He received the “Masters in Physics” degree from Bharathiyar University, Coimbatore, India in 2001. In 2002, he joined Defense Research and Development organization (DRDO) as Scientist and currently working in Department of Radiodiagnosis and Imaging, Armed Forces Medical College, Pune, India. His research interests are artificial neural networks, image segmentation, image vision, multiresolution analysis.

    Gharpure Damayanti received the MSc and PhD degrees from Pune University in 1984 and 1992, respectively. She joined the Department of Electronic Science, University of Pune, India in 1988. Since then she has worked on a number of research projects and consultancy projects related to machine vision applications, design of embedded E-nose, hardware implementation of Automatic trajectory tracking system, etc. Her current research interests include applications of neural networks for pattern recognition, image analysis and segmentation, odor analysis and embedded system design.

    Rochan Pant was born in Dhanbad, India, in 1966. He completed his Bachelor's Degree in Medicine (MBBS) in 1988 from the Armed Forces Medical College, Pune, India. He was commissioned into the Indian Navy. He completed his postgraduate studies and received a Master's Degree (MD) in Radiodiagnosis in 1995 from the Armed Forces Medical College, Pune, India. He completed a 2-year fellowship in Interventional Radiology at the Bombay Institute of Medical Sciences, Mumbai, India. He is at present Associate Professor at the Department of Radiodiagnosis and Imaging, Armed Forces Medical College, Pune, India. His research interests are neurovascular interventions, cerebral perfusion studies, and renovascular disease.

    C.M. Sreedhar was born in Coimbatore, India, in 1965. He completed his Bachelor's Degree in Medicine (MBBS) in 1986 from the Armed Forces Medical College, Pune, India. He was commissioned into the Indian Army. He completed his postgraduate studies and received a Master's Degree (MD) in Radiodiagnosis in 1993 from the Armed Forces Medical College, Pune, India. He received a year's training in MRI and CT techniques at Calcutta, India. He is at present Associate Professor at the Department of Radiodiagnosis and Imaging, Armed Forces Medical College, Pune, India. His research interests are Imaging of Cerebral Tumours, Diffusion and Perfusion imaging in the brain, Imaging of CNS infections in AIDS, MRI in congenital heart disease, and peripheral vascular imaging.

    View full text