Robust Depth Image Acquisition Using Modulated Pattern Projection and Probabilistic Graphical Models.

Depth image acquisition with structured light approaches in outdoor environments is a challenging problem due to external factors, such as ambient sunlight, which commonly affect the acquisition procedure. This paper presents a novel structured light sensor designed specifically for operation in outdoor environments. The sensor exploits a modulated sequence of structured light projected onto the target scene to counteract environmental factors and estimate a spatial distortion map in a robust manner. The correspondence between the projected pattern and the estimated distortion map is then established using a probabilistic framework based on graphical models. Finally, the depth image of the target scene is reconstructed using a number of reference frames recorded during the calibration process. We evaluate the proposed sensor on experimental data in indoor and outdoor environments and present comparative experiments with other existing methods, as well as commercial sensors.

Modulated acquisition of spatial distortion maps.

This work discusses a novel approach to image acquisition which improves the robustness of captured data required for 3D range measurements. By applying a pseudo-random code modulation to sequential acquisition of projected patterns the impact of environmental factors such as ambient light and mutual interference is significantly reduced. The proposed concept has been proven with an experimental range sensor based on the laser triangulation principle. The proposed design can potentially enhance the use of this principle to a variety of outdoor applications, such as autonomous vehicles, pedestrians' safety, collision avoidance, and many other tasks, where robust real-time distance detection in real world environment is crucial.

Image cytometric nuclear texture features in inoperable head and neck cancer: a pilot study.

BACKGROUND: Image cytometry can measure numerous nuclear features which could be considered a surrogate end-point marker of molecular genetic changes in a nucleus. The aim of the study was to analyze image cytometric nuclear features in paired samples of primary tumor and neck metastasis in patients with inoperable carcinoma of the head and neck. MATERIALS AND METHODS.: Image cytometric analysis of cell suspensions prepared from primary tumor tissue and fine needle aspiration biopsy cell samples of neck metastases from 21 patients treated with concomitant radiochemotherapy was performed. Nuclear features were correlated with clinical characteristics and response to therapy. RESULTS: Manifestation of distant metastases and new primaries was associated (p<0.05) with several chromatin characteristics from primary tumor cells, whereas the origin of index cancer and disease response in the neck was related to those in the cells from metastases. Many nuclear features of primary tumors and metastases correlated with the TNM stage. CONCLUSIONS: A specific pattern of correlation between well-established prognostic indicators and nuclear features of samples from primary tumors and those from neck metastases was observed. Image cytometric nuclear features represent a promising candidate marker for recognition of biologically different tumor subgroups.

Malignancy associated changes in epithelial cells of buccal mucosa: a potential cancer detection test.

OBJECTIVE: To analyze the presence of malignancy associated changes (MACs) in normal buccal mucosa cells of lung and breast cancer patients and their relationship to tumor subtype, stage and size. STUDY DESIGN: Buccal mucosa smears of 107 lung cancer and 100 breast cancer patients and corresponding healthy subjects were collected, stained by the DNA-specific Feulgen-thionin method and scanned using an automated high-resolution cytometer. Nuclear texture features of a minimum of 500 nuclei per slide were calculated, and statistical classifiers using Gaussian models of class-probability distribution were designed, trained and tested in 3 parts: (1) ability to separate cancer patient samples from controls, (2) cross-validation of classifiers for different cancer types, and (3) correlation of MAC expression with tumor subtype, stage and size. RESULTS: Lung and breast cancer induce MACs in normal buccal mucosa cells. The classifiers based on the selected nuclear features correctly recognized >80% of lung and breast cancer cases. The results indicate that MAC detection is not dependent on the tumor subtype, stage or size. CONCLUSION: The presence of MACs in buccal mucosa cells offers the potential for developing a new noninvasive cancer screening test.

Value of image cytometry in the subclassification of rhabdomyosarcoma.

OBJECTIVE: To test the discriminatory capability of nuclear features in the subclassification of rhabdomyosarcoma (RMS) and especially to differentiate embryonal from alveolar RMS. STUDY DESIGN: The study included 42 patients with RMS. We performed the analysis on Feulgen-stained filtrates of cell suspensions prepared from deparaffinized tissue sections. Image analysis was performed by an automated, high-resolution image cytometer on at least 200 nuclear images. Photometric, morphometric and nuclear texture features were analyzed. Probability density distributions were calculated for each nuclear feature of individual RMS subgroups and compared in order to detect possible differences. RESULTS: There were significant differences between embryonal and alveolar RMS in five nuclear features: DNA index, sphericity, elongation, low_DNA_area and fractall_area. We were able to differentiate between the two main RMS subgroups in 82% of cases on the basis of either sphericity or elongation alone, while the power of differentiation for texture features was 72-79%. CONCLUSION: Differentiation between embryonal and alveolar RMS using one nuclear feature is not an important adjunct to light microscopy. However, the possibility of using a combination of nuclear features would probably increase the discriminatory ability.