Eleftheria A. Mylona

Unsupervised 2D gel electrophoresis image segmentation based on active contours

This work introduces a novel active contour-based scheme for unsupervised segmentation of protein spots in two-dimensional gel electrophoresis (2D-GE) images. The proposed segmentation scheme is the first to exploit the attractive properties of the active contour formulation in order to cope with crucial issues in 2D-GE image analysis, including the presence of noise, streaks, multiplets and faint spots. In addition, it is unsupervised, providing an alternate to the laborious, error-prone process of manual editing, which is required in state-of-the-art 2D-GE image analysis software packages. It is based on the formation of a spot-targeted level-set surface, as well as of morphologically-derived active contour energy terms, used to guide active contour initialization and evolution, respectively. The experimental results on real and synthetic 2D-GE images demonstrate that the proposed scheme results in more plausible spot boundaries and outperforms all commercial software packages in terms of segmentation quality.

Automated Adjustment of Region-Based Active Contour Parameters Using Local Image Geometry

A principled method for active contour (AC) parameterization remains a challenging issue in segmentation research, with a potential impact on the quality, objectivity, and robustness of the segmentation results. This paper introduces a novel framework for automated adjustment of region-based AC regularization and data fidelity parameters. Motivated by an isomorphism between the weighting factors of AC energy terms and the eigenvalues of structure tensors, we encode local geometry information by mining the orientation coherence in edge regions. In this light, the AC is repelled from regions of randomly oriented edges and guided toward structured edge regions. Experiments are performed on four state-of-the-art AC models, which are automatically adjusted and applied on benchmark datasets of natural, textured and biomedical images and two image restoration models. The experimental results demonstrate that the obtained segmentation quality is comparable to the one obtained by empirical parameter adjustment, without the cumbersome and time-consuming process of trial and error.

Entropy-based spatially-varying adjustment of active contour parameters

Parameter adjustment is a crucial, open issue in active contour methodology. Most state-of-the-art active contours are empirically adjusted on a trial and error basis. Such an empirical approach lacks scientific foundation, leads to suboptimal segmentation results and requires technical skills from the end-user. This work introduces a method for automatic adjustment of active contour parameters, which is based on image entropy. In addition, instead of being uniform, the parameter values calculated are spatially-varying, so as to reflect textural variations over the image. Experimental evaluation of the proposed method is conducted on thyroid US images, liver MRI images, as well as on real-world photographs. The results indicate that the proposed method is capable of identifying plausible object boundaries, obtaining a segmentation quality which is comparable to the one obtained with empirical parameter adjustment. Moreover, the applicability of the proposed method is not confined on a single active contour variation.

A Computer-Based Technique for Automated Spot Detection in Proteomics Images

This paper introduces a novel computer-based technique for automated detection of protein spots in proteomics images. The proposed technique is based on the localization of regional intensity maxima associated with protein spots and is formulated so as to ignore rectangular-shaped streaks, minimize the detection of false negatives, and allow the detection of multiple overlapping spots. Regional intensity constraints are imposed on the localized maxima in order to cope with the presence of noise and artifacts. The experimental evaluation of the proposed technique on real proteomics images demonstrates that it: 1) achieves a predictive value ( PV) and detection sensitivity (DS ) which exceed 90%; 2) outperforms Melanie software package in terms of PV , specificity, and DS; 3) ignores artifacts; 4) distinguishes multiple overlapping spots; 5) locates spots within streaks; and 6) is automated and efficient.

Segmentation of two-dimensional gel electrophoresis images containing overlapping spots

This work addresses the segmentation of two-dimensional polyacrylamide gel electrophoresis images containing overlapping protein spots. A novel segmentation approach is proposed, which is capable of detecting spot boundaries within the region of overlap. The proposed approach is based on the observation that the spot boundaries in the overlap region are associated with local intensity minima. The experimental evaluation of the proposed approach demonstrates that it is capable of identifying multiple overlapping spots, as opposed to state of the art segmentation approaches. Moreover, it results in more accurate spot delineations, when compared to Progenesis SameSpots.

A two-stage active contour-based scheme for spot detection in proteomics images

This work introduces a two-stage active contour-based scheme for the detection of protein spots in two-dimensional gel electrophoresis images. The proposed scheme is formulated as an expansion of the Chan-Vese model and is capable of distinguishing overlapping spots. Moreover, it remains unaffected in the presence of noise and the inhomogeneous background that characterize these images. The experimental results demonstrate that the proposed scheme obtains more plausible spot boundaries than PDQuest (BioRad)1.

Protein spot detection in 2D-GE images using morphological operators

This work addresses the detection of protein spots in 2D gel electrophoresis images. A novel morphology-based approach is proposed, which utilizes the dilation operator for the localization of regional intensity maxima associated with protein spots. A disk-shaped structuring element (SE) is selected in agreement with the prevalent roundish shape of the majority of protein spots. Thus, spots within rectangular-shaped streaks are correctly detected. SE size is set considering that a certain radius value allows the discrimination of individual spots located in complex spot regions as well as small-sized spots. Moreover, spurious intensity maxima associated with noise are ignored by applying regional intensity constraints. The results of the experimental evaluation lead to the conclusion that the proposed approach detects more actual protein spots and less false spots than a renowned 2D-GE image analysis software package. Furthermore, it does not require user intervention.

Self-parameterized active contours based on regional edge structure for medical image segmentation

This work introduces a novel framework for unsupervised parameterization of region-based active contour regularization and data fidelity terms, which is applied for medical image segmentation. The work aims to relieve MDs from the laborious, time-consuming task of empirical parameterization and bolster the objectivity of the segmentation results. The proposed framework is inspired by an observed isomorphism between the eigenvalues of structure tensors and active contour parameters. Both may act as descriptors of the orientation coherence in regions containing edges. The experimental results demonstrate that the proposed framework maintains a high segmentation quality without the need of trial-and-error parameter adjustment.

Towards Self-Parameterized Active Contours for Medical Image Segmentation with Emphasis on Abdomen

Medical doctors are typically required to segment medical images by means of computational tools, which suffer from parameters that are empirically selected through a cumbersome and time-consuming process. This chapter presents a framework for automated parameterization of region-based active contour regularization and data fidelity terms, which aims to relieve medical doctors from this process, as well as to enhance objectivity and reproducibility. Leaned on an observed isomorphism between the eigenvalues of structure tensors and active contour parameters, the presented framework automatically adjusts active contour parameters so as to reflect the orientation coherence in edge regions by means of the “orientation entropy.” To this end, the active contour is repelled from randomly oriented edge regions and is navigated towards structured ones, accelerating contour convergence. Experiments are conducted on abdominal imaging domains, which include colon and lung images. The experimental evaluation demonstrates that the presented framework is capable of speeding up contour convergence, whereas it achieves high-quality segmentation results, albeit in an unsupervised fashion.

Self-adjusted active contours using multi-directional texture cues

Parameterization is an open issue in active contour research, associated with the cumbersome and time-consuming process of empirical adjustment. This work introduces a novel framework for self-adjustment of region-based active contours, based on multi-directional texture cues. The latter are mined by applying filtering transforms characterized by multi-resolution, anisotropy, localization and directionality. This process yields to entropy-based image “heatmaps”, used to weight the regularization and data fidelity terms, which guide contour evolution. Experimental evaluation is performed on a large benchmark dataset as well as on textured images. The segmentation results demonstrate that the proposed framework is capable of accelerating contour convergence, maintaining a segmentation quality which is comparable to the one obtained by empirically adjusted active contours.