Tissue segmentation is one of the key preliminary steps in the morphometric analysis of tissue architecture. In multi-channel
immunoflurorescent biomarker images, the primary segmentation steps consist of segmenting the nuclei
(epithelial and stromal) and epithelial cytoplasm from 4',6-diamidino-2-phenylindole (DAPI) and cytokeratin 18 (CK18)
biomarker images respectively. The epithelial cytoplasm segmentation can be very challenging due to variability in
cytoplasm morphology and image staining. A robust and adaptive segmentation algorithm was developed for the
purpose of both delineating the boundaries and separating thin gaps that separate the epithelial unit structures. This
paper discusses novel methods that were developed for adaptive segmentation of epithelial cytoplasm and separation of
epithelial units. The adaptive segmentation was performed by computing the non-epithelial background texture of every
CK18 biomarker image. The epithelial unit separation was performed using two complementary techniques: a marker
based, center-initialized watershed transform and a boundary initialized fast marching-watershed segmentation. The
adaptive segmentation algorithm was tested on 926 CK18 biomarker biopsy images (326 patients) with limited
background noise and 1030 prostatectomy images (374 patients) with noisy to very noisy background. The segmentation
performance was measured using two different methods, namely; stability and background textural metrics. It was
observed that the database of 1030 noisy prostatectomy images had a lower mean value (using stability and three
background texture performance metrics) compared to the biopsy dataset of 926 images that had limited background
noise. The average of all four performance metrics yielded 94.32% accuracy for prostatectomy images compared to
99.40% accuracy for biopsy images.
Accurate segmentation of overlapping nuclei is essential in determining nuclei count and evaluating the sub-cellular
localization of protein biomarkers in image Cytometry and Histology. Current cellular segmentation algorithms generally
lack fast and reliable methods for disambiguating clumped nuclei. In immuno-fluorescence segmentation, solutions to
challenges including nuclei misclassification, irregular boundaries, and under-segmentation require reliable separation of
clumped nuclei. This paper presents a fast and accurate algorithm for joint segmentation of cellular cytoplasm and nuclei
incorporating procedures for reliably separating overlapping nuclei. The algorithm utilizes a combination of ideas and is
a significant improvement on state-of-the-art algorithms for this application. First, an adaptive process that includes top-hat
filtering, blob detection and distance transforms estimates the inverse illumination field and corrects for intensity
non-uniformity. Minimum-error-thresholding based binarization augmented by statistical stability estimation is applied
prior to seed-detection constrained by a distance-map-based scale-selection to identify candidate seeds for nuclei
segmentation. The nuclei clustering step also incorporates error estimation based on statistical stability. This enables the
algorithm to perform localized error correction. Final steps include artifact removal and reclassification of nuclei objects
near the cytoplasm boundary as epithelial or stroma. Evaluation using 48 realistic phantom images with known ground-truth
shows overall segmentation accuracy exceeding 96%. It significantly outperformed two state-of-the-art algorithms
in clumped nuclei separation. Tests on 926 prostate biopsy images (326 patients) show that the segmentation
improvement improves the predictive power of nuclei architecture features based on the minimum spanning tree
algorithm. The algorithm has been deployed in a large scale pathology application.
The scattering centers in cells are not spheres, however, in most modeling of light transport, the scattering centers are assumed to be spherical. For example, in Monte Carlo simulations a Mie or Henyey-Greenstein phase function is often used. It is known that an elliptical particle will have a different phase function than a spherical particle. In particular there are differences in the phase functions for scattering polarized light. To examine how these changes in phase function affect light transport in tissue, we have developed a Monte Carlo code for light transport that uses elliptical scatterers. The phase functions are calculated using a T-matrix code and the propagation of polarized photons is performed in a manner analagous to that used by Bartel and Hielscher. Our initial results indicate that for narrow particle distributions the difference in shape can cause large differences in the intensity and polarization properties of the diffusely reflected light. For a mixture of particle sizes, however, there is a much smaller difference in the properties of the diffusely scattered light. Results are presented for both narrow and broad distributions of scatter sizes relevant to tissue.
A Computer-assisted Chest Radiograph Reader System (CARRS) was developed for the detection of pathological features in lungs presenting with pneumoconioses. CARRS applies novel techniques in automatic image segmentation, incorporates neural network-based pattern classification, and integrates these into a graphical user interface. The three aspects of CARRS are described: Chest radiograph digitization and display, rib and parenchyma characterization, and classification. The quantization of the chest radiograph film was optimized to maximize the information content of the digital images. Entropy was used as the benchmark for optimizing the quantization. From the rib-segmented images, regions of interest were selected by the pulmonologist. A feature vector composed of image characteristics such as entropy, textural statistics, etc. was calculated. A laterally primed adaptive resonance theory (LAPART) neural network was used as the classifier. LAPART classification accuracy averaged 86.8 %. Truth was determined by the two pulmonologists. The CARRS has demonstrated potential as a screening device. Today, 90% or more of the chest radiographs seen by the pulmonologist are normal. A computer-based system that can screen 50% or more of the chest radiographs represents a large savings in time and dollars.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.