Prof. Mauro Tambasco Profile

Prof. Mauro Tambasco

Professor at Tom Baker Cancer Ctr

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Proceedings Article | 28 February 2009 Paper

Segmentation of histological structures for fractal analysis

Vanessa Dixon, Alexei Kouznetsov, Mauro Tambasco

Proceedings Volume 7260, 72603E (2009) https://doi.org/10.1117/12.812437

KEYWORDS: Fractal analysis, Image segmentation, Binary data, Detection and tracking algorithms, Edge detection, Cancer, Image processing algorithms and systems, Prostate, Sensors, Tumors

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Pathologists examine histology sections to make diagnostic and prognostic assessments regarding cancer based on deviations in cellular and/or glandular structures. However, these assessments are subjective and exhibit some degree of observer variability. Recent studies have shown that fractal dimension (a quantitative measure of structural complexity) has proven useful for characterizing structural deviations and exhibits great potential for automated cancer diagnosis and prognosis. Computing fractal dimension relies on accurate image segmentation to capture the architectural complexity of the histology specimen. For this purpose, previous studies have used techniques such as intensity histogram analysis and edge detection algorithms. However, care must be taken when segmenting pathologically relevant structures since improper edge detection can result in an inaccurate estimation of fractal dimension. In this study, we established a reliable method for segmenting edges from grayscale images. We used a Koch snowflake, an object of known fractal dimension, to investigate the accuracy of various edge detection algorithms and selected the most appropriate algorithm to extract the outline structures. Next, we created validation objects ranging in fractal dimension from 1.3 to 1.9 imitating the size, structural complexity, and spatial pixel intensity distribution of stained histology section images. We applied increasing intensity thresholds to the validation objects to extract the outline structures and observe the effects on the corresponding segmentation and fractal dimension. The intensity threshold yielding the maximum fractal dimension provided the most accurate fractal dimension and segmentation, indicating that this quantitative method could be used in an automated classification system for histology specimens.

Proceedings Article | 30 March 2007 Paper

Fractal analysis for assessing tumour grade in microscopic images of breast tissue

Mauro Tambasco, Meghan Costello, Chris Newcomb, Anthony Magliocco

Proceedings Volume 6514, 651422 (2007) https://doi.org/10.1117/12.713660

KEYWORDS: Fractal analysis, Tumors, Breast, Breast cancer, Cancer, Tissues, Binary data, Statistical analysis, Gold, Oncology

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In 2006, breast cancer is expected to continue as the leading form of cancer diagnosed in women, and the second leading cause of cancer mortality in this group. A method that has proven useful for guiding the choice of treatment strategy is the assessment of histological tumor grade. The grading is based upon the mitosis count, nuclear pleomorphism, and tubular formation, and is known to be subject to inter-observer variability. Since cancer grade is one of the most significant predictors of prognosis, errors in grading can affect patient management and outcome. Hence, there is a need to develop a breast cancer-grading tool that is minimally operator dependent to reduce variability associated with the current grading system, and thereby reduce uncertainty that may impact patient outcome. In this work, we explored the potential of a computer-based approach using fractal analysis as a quantitative measure of cancer grade for breast specimens. More specifically, we developed and optimized computational tools to compute the fractal dimension of low- versus high-grade breast sections and found them to be significantly different, 1.3±0.10 versus 1.49±0.10, respectively (Kolmogorov-Smirnov test, p<0.001). These results indicate that fractal dimension (a measure of morphologic complexity) may be a useful tool for demarcating low- versus high-grade cancer specimens, and has potential as an objective measure of breast cancer grade. Such prognostic value could provide more sensitive and specific information that would reduce inter-observer variability by aiding the pathologist in grading cancers.

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