Research
Biomedical Imaging
The current plethora of imaging technologies such as magnetic resonance imaging (MR), computed tomography (CT), position emission tomography (PET) and ultrasound provide great insight into the different anatomical and functional processes of the human body. While such imaging technologies have improved significantly over the years, there are still many challenging tasks to be addressed in the analysis of biomedical images.
Typically, the acquired data can be largely unusable in raw form due to factors such as noise, technology-related artifacts, poor resolution, and contrast, hence low-level image processing tasks such as contrast enhancement and noise removal are required. Furthermore, given the complexities of biomedical imaging data, it is often difficult for research scientists and clinicians to interpret and analyze the acquired data in a meaningful and efficient fashion. Therefore high-level image processing tasks related to automatic classification and description of the image content are becoming urgent.
At the CILAB we developed methods to address the uncertainty and imprecision characterizing both low-level and high-level processing tasks associated with biomedical imaging to assist clinicians, radiologists, pathologists, and clinical research scientists in better visualizing, diagnosing, and understanding various diseases affecting the human body.
Publications
L. Caponetti, G. Castellano, V. Corsini. Mr brain image segmentation: A framework to compare different clustering techniques. Information, 8(4):138–159, 2017. DOI:10.3390/info8040138.
L. Caponetti, G. Castellano, M.T. Basile, V. Corsini. Fuzzy mathematical morphology for biological image segmentation. Applied Intelligence, 40(1):1–11, January 2014. DOI: 10.1007/s10489-013-0509-6.
G. Sforza, G. Castellano, S. Arika, R.W. LeAnder, R.J. Stanley William, V. Stoecker, J.R. Hagerty. Using adaptive thresholding and skewness correction to detect gray areas in melanoma in situ images. IEEE Transactions on Instrumentation and Measurement, 61(7):1839–1847, 2012. DOI: 10.1109/TIM.2012.2192349.
G. Sforza, G. Castellano, R. Joe Stanley, W.V. Stoecker, J. Hagerty. Adaptive segmentation of gray areas in dermoscopy images. In Proc. of the 6th International Symposium on Medical Measurement and Applications (MEMEA 2011), pp. 628–631, Bari, Italy, May 2011. DOI: 10.1109/MeMeA.2011.5966741.
L. Caponetti, G. Castellano, V. Corsini, T.M.A. Basile. Cytoplasm image segmentation by spatial fuzzy clustering. In Fuzzy Logic and Applications. Volume 6857 Lecture Notes in Computer Science (LNCS), pp. 253–260. Springer-Verlag, 2011. DOI: 10.1007/978-3-642- 23713-3_32.
M.T.A. Basile, L. Caponetti, G. Castellano, G. Sforza. A texture-based image processing approach for the description of human oocyte cytoplasm. IEEE Trans. on Instrumentation and Measurement, 59(10):2591–2601, 2010. DOI:10.1109/TIM.2010.2057552.
Developed methods
a framework for MR brain image segmentation to support the expert in identifying different brain regions for further analysis. The framework includes different clustering methods to perform segmentation of MR images and enables easy comparison of different segmentation results by providing a quantitative evaluation using an entropy-based measure as well as other measures commonly used to evaluate segmentation results.
a method for oocyte image segmentation to detect automatically the cytoplasm region for further analysis of oocyte maturity which may be directly related to the success rate of Intra Cytoplasmic Sperm Injection (ICSI) technologies.
a method for dermoscopy skin image segmentation to detect gray areas, which are one main characteristic of malignant melanoma.