Detecting flash artifacts in fundus imagery.

In a telemedicine environment for retinopathy screening, a quality check is needed on initial input images to ensure sufficient clarity for proper diagnosis. This is true whether the system uses human screeners or automated software for diagnosis. We present a method for the detection of flash artifacts found in retina images. We have collected a set of retina fundus imagery from February 2009 to August 2011 from several clinics in the mid-South region of the USA as part of a telemedical project. These images have been screened with a quality check that sometimes omits specific flash artifacts, which can be detrimental for automated detection of retina anomalies. A multi-step method for detecting flash artifacts in the center area of the retina was created by combining characteristic colorimetric information and morphological pattern matching. The flash detection was tested on a dataset of 5218 images representative of the population. The system achieved a sensitivity of 96.54% and specificity of 70.16% for the detection of the flash artifacts. The flash artifact detection can serve as a useful tool in quality screening of retina images in a telemedicine network. The detection can be expected to improve automated detection by either providing special handling for these images in combination with a flash mitigation or removal method.

A 3D level sets method for segmenting the mouse spleen and follicles in volumetric microCT images.

We present a semi-automatic, 3D approach for segmenting the mouse spleen, and its interior follicles, in volumetric microCT imagery. Based upon previous 2D level sets work, we develop a fully 3D implementation and provide the corresponding finite difference formulas. We incorporate statistical and proximity weighting schemes to improve segmentation performance. We also note an issue with the original algorithm and propose a solution that proves beneficial in our experiments. Experimental results are provided for artificial and real data.

Quantitative comparison of mitotic spindles by confocal image analysis.

The mitotic spindle is a subcellular protein structure that facilitates chromosome segregation and is crucial to cell division. We describe an image processing approach to quantitatively characterize and compare mitotic spindles that have been imaged three dimensionally using confocal microscopy with fixed-cell preparations. The proposed approach is based on a set of features that are computed from each image stack representing a spindle. We compare several spindle datasets of varying biological (genotype) and/or environmental (drug treatment) conditions. The goal of this effort is to aid biologists in detecting differences between spindles that may not be apparent under subjective visual inspection, and furthermore, to eventually automate such analysis in high-throughput scenarios (thousands of images) where manual inspection would be unreasonable. Experimental results on positive- and negative-control data indicate that the proposed approach is indeed effective. Differences are detected when it is known they do exist (positive control) and no differences are detected when there are none (negative control). In two other experimental comparisons, results indicate structural spindle differences that biologists had not observed previously.

Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images.

The lungs exchange air with the external environment via the pulmonary airways. Computed tomography (CT) scanning can be used to obtain detailed images of the pulmonary anatomy, including the airways. These images have been used to measure airway geometry, study airway reactivity, and guide surgical interventions. Prior to these applications, airway segmentation can be used to identify the airway lumen in the CT images. Airway tree segmentation can be performed manually by an image analyst, but the complexity of the tree makes manual segmentation tedious and extremely time-consuming. We describe a fully automatic technique for segmenting the airway tree in three-dimensional (3-D) CT images of the thorax. We use grayscale morphological reconstruction to identify candidate airways on CT slices and then reconstruct a connected 3-D airway tree. After segmentation, we estimate airway branchpoints based on connectivity changes in the reconstructed tree. Compared to manual analysis on 3-mm-thick electron-beam CT images, the automatic approach has an overall airway branch detection sensitivity of approximately 73%.