3-D in vivo brain tumor geometry study by scaling analysis (original) (raw)
Related papers
Brain Tumors: A Scaling Analysis Approach
2012
A new method, based in scaling analysis, is used to calculate fractal dimension and local roughness exponent to characterize in vivo 3-D tumor growth in brain. Image acquisition was made according to the standard protocol used for brain radiotherapy and radiosurgery, i.e., axial, coronal and sagittal T 1 -weighted images, comprising brain volume for further magnetic resonance image (MRI) registration. Image segmentation was performed by application of kmeans procedure upon contrasted images. Tumors analyzed included glioblastomas, astrocytomas, metastases and benign brain tumors. The results show significant variations of the parameters according to tumor stage and histological origin.
Geometrical study of astrocytomas through fractals and scaling analysis
Applied Radiation and Isotopes
The tumor growth is a complex process characterized by the proliferation of uncontrollable cells which invade neighbor tissues. The understanding process of this type of phenomena is very relevant in order to establish diagnosis and proper therapy strategies and to start the valorization of its complexity with proper descriptors produced by the scaling analysis, which define the tumor growth geometry. In this work, obtained results through the scaling analysis for pilocytic astrocytomas, anaplastic and diffuse, are shown, which tumors of primary origin are. On them, it is calculated the fractal dimension and critical exponents of local roughness to characterize in vivo 3-D tumor growth. The acquisition of the images for this type of injuries was carried out according to the standard protocol used for brain radiotherapy and radiosurgery, i.e., axial, coronal and sagittal magnetic resonance T1 weighted images and comprising the brain volume for image registration. Image segmentation was performed by the application the k-means procedure upon contrasted images. The results show significant variations of the parameters depending on the tumor stage and its histological origin.
Evaluation of malignancy in tumors of the central nervous system using fractal dimension
2000
In this work, we propose the use of the concepts of Fractal Dimension and Digital Image Processing, as a possible methodology to characterize the degree of malignancy of neoplastic structures located in the Central Nervous System. The images were detected by Magnetic Resonance Imaging (MRI) techniques including Proton Density, T 1 , and T 2 images. Malignant lesions (Gliomas) were compared with benign ones (Cysts). The Correlation Dimension, Lyapunov Exponents and Information Dimension were used as the relevant geometrical properties to characterize the irregular edge present in a particular structure. The edge was obtained by means of an edge detector operator and afterwards a codification procedure based on Fourier Descriptors was used to generate a numerical array or Time Series. The analysis of the processed images revealed that the relevant geometrical properties exhibit a different behavior in the case of gliomas compared to cystic lesions, a fact that can be used by the physician as an auxiliary tool to evaluate the malignancy of neoplastic structures in the brain.
Fractal Properties and Critical Exponents for Tumor Staging and Classification
In general, tumors exhibit irregular borders with geometrical properties which are expected to depend upon their degree of malignancy. To appropriately evaluate these irregularities, it is necessary to apply segmentation procedures on the image to clearly define the active region of the tumor and its border. In the present work, T1 and T2 weighted magnetic resonance images of brain tumors were used to construct a nosologic map using in vivo magnetic resonance spectroscopy and biopsy for tissue classification and reference tumor staging. Different segmentation procedures were performed on the images, including gray level threshold and deformable contours (snakes). Several fractal properties were determined on the contour: the capacity or fractal dimension and the correlation dimension for different time series constructed from the original contour. Also a critical exponent coming from the contour roughness was calculated. The results obtained showed a good correlation between the rou...
Morphological and Fractal Properties of Brain Tumors
Frontiers in Physiology
Tumor interface dynamics is a complex process determined by cell proliferation and invasion to neighboring tissues. Parameters extracted from the tumor interface fluctuations allow for the characterization of the particular growth model, which could be relevant for an appropriate diagnosis and the correspondent therapeutic strategy. Previous work, based on scaling analysis of the tumor interface, demonstrated that gliomas strictly behave as it is proposed by the Family-Vicsek ansatz, which corresponds to a proliferative-invasive growth model, while for meningiomas and acoustic schwannomas, a proliferative growth model is more suitable. In the present work, other morphological and dynamical descriptors are used as a complementary view, such as surface regularity, one-dimensional fluctuations represented as ordered series and bi-dimensional fluctuations of the tumor interface. These fluctuations were analyzed by Detrended Fluctuation Analysis to determine generalized fractal dimension...
MODELING OF BRAIN CANCER DEVELOPMENT USING FRACTAL GEOMETRY
This investigation attempts to analyze the growth of brain cancerous tissue employing the technique of fractal geometry, specifically computing the fractal dimensions. The fractal dimensions were determined using a box-counting method. The earliest stage showed lowest fractal dimension (D), 1.6541. The intermediate stage showed maximum fractal dimension of 1.7016 while the last stage available showed slightly lower D value of 1.6847. These results were found to be in agreement with those of previous studies on certain cancer types. The use of fractal analysis in the diagnosis of cancer is discussed. It has been shown that the fractal dimension of 2D microvasculature networks can discriminate between normal versus tumor tissue. Fractal dimensions also differed between various stages of malignancy.
Fractal properties and critical exponents in tumor
Ciencia, 2011
In general, tumors exhibit irregular borders with geometrical properties which are expected to depend upon their degree of malignancy. To appropriately evaluate these irregularities, it is necessary to apply segmentation procedures on the image that clearly define the active region of the tumor and its border. In the present work, nosologic maps were obtained combining T 2 -weighted magnetic resonance images with in vivo magnetic resonance spectroscopy information on brain tumors. The image was segmented according to the nosologic map and tumor contours were determined. Several fractal properties were determined on the contour: the capacity or fractal dimension, the correlation dimension for a temporal series constructed from the original contour, and also a critical exponent coming from the contour roughness. The results obtained showed a good correlation between the roughness critical exponent and the degree of malignancy of the tumor.
Fractal analysis of tumor in brain MR images
Machine Vision and Applications - MVA, 2003
Abstract. The purpose of this study is to discuss existing fractal-based algorithms and propose novel improvements of these algorithms to identify tumors in brain magnetic-response (MR) images. Considerable research has been pursued on fractal geometry in various aspects of image analysis and pattern recognition. Magnetic-resonance images typically have a degree of noise and randomness associated with the natural random nature of structure. Thus, fractal analysis is appropriate for MR image analysis. For tumor detection, we describe existing fractal-based techniques and propose three modified algorithms using fractal analysis models. For each new method, the brain MR images are divided into a number of pieces. The first method involves thresholding the pixel intensity values; hence, we call the technique piecewise-threshold-box-counting (PTBC) method. For the subsequent methods, the intensity is treated as the third dimension. We implement the improved piecewise-modified-box-coun...
Neuroradiology, 2012
Introduction Susceptibility-weighted imaging (SWI) with high-and ultra-high-field magnetic resonance is a very helpful tool for evaluating brain gliomas and intratumoral structures, including microvasculature. Here, we test whether objective quantification of intratumoral SWI patterns by applying fractal analysis can offer reliable indexes capable of differentiating glial tumor grades. Methods Thirty-six patients affected by brain gliomas (grades II-IV, according to the WHO classification system) underwent MRI at 7 T using a SWI protocol. All images were collected and analyzed by applying a computer-aided fractal image analysis, which applies the fractal dimension as a measure of geometrical complexity of intratumoral SWI patterns. The results were subsequently statistically correlated to the histopathological tumor grade. Results The mean value of the fractal dimension of the intratumoral SWI patterns was 2.086±0.413. We found a trend of higher fractal dimension values in groups of higher histologic grade. The values ranged from a mean value of 1.682±0.278 for grade II gliomas to 2.247±0.358 for grade IV gliomas (p00.013); there was an overall statistically significant difference between histopathological groups. Conclusion The present study confirms that SWI at 7 T is a useful method for detecting intratumoral vascular architecture of brain gliomas and that SWI pattern quantification by means of fractal dimension offers a potential objective morphometric image biomarker of tumor grade.
BACKGROUND: Susceptibility-weighted imaging (SWI) of brain tumors provides information about neoplastic vasculature and intratumoral micro-and macrobleedings. Low-and high-grade gliomas can be distinguished by SWI due to their different vascular characteristics. Fractal analysis allows for quantification of these radiological differences by a computer based morphological assessment of SWI patterns. OBJECTIVE: To show the feasibility of SWI analysis on 3-T magnetic resonance imaging to distinguish different kinds of brain tumors. METHODS: Seventy-eight patients affected by brain tumors of different histopathology (low-and high-grade gliomas, metastases, meningiomas, lymphomas) were included. All patients underwent preoperative 3-T magnetic resonance imaging including SWI, on which the lesions were contoured. The images underwent automated computation, extracting 2 quantitative parameters: the volume fraction of SWI signals within the tumors (signal ratio) and the morphological self-similar features (fractal dimension [FD]). The results were then correlated with each histopathological type of tumor. RESULTS: Signal ratio and FD were able to differentiate low-grade gliomas from grade III and IV gliomas, metastases, and meningiomas (P , .05). FD was statistically different between lymphomas and high-grade gliomas (P , .05). A receiver-operating characteristic analysis showed that the optimal cutoff value for differentiating low-from high-grade gliomas was 1.75 for FD (sensitivity, 81%; specificity, 89%) and 0.03 for signal ratio (sensitivity, 80%; specificity, 86%). CONCLUSION: FD of SWI on 3-T magnetic resonance imaging is a novel image bio-marker for glioma grading and brain tumor characterization. Computational models offer promising results that may improve diagnosis and open perspectives in the radiological assessment of brain tumors.