Automatic estimation of wheat grain morphometry from computed tomography data.

Wheat (Triticum aestivum L.) grain size and morphology are playing an increasingly important role as agronomic traits. Whole spikes from two disparate strains, the commercial type Capelle and the landrace Indian Shot Wheat, were imaged using a commercial computed tomography system. Volumetric information was obtained using a standard back-propagation approach. To extract individual grains within the spikes, we used an image processing pipeline that included adaptive thresholding, morphological filtering, persistence aspects and volumetric reconstruction. This is a fully automated, data-driven pipeline. Subsequently, we extracted several morphometric measures from the individual grains. Taking the location and morphology of the grains into account, we show distinct differences between the commercial and landrace types. For example, average volume is significantly greater for the commercial type (P=0.0024), as is the crease depth (P=1.61×10-5). This pilot study shows that the fully automated approach described can retain developmental information and reveal new morphology information at an individual grain level.

Topological modeling and classification of mammographic microcalcification clusters.

GOAL: The presence of microcalcification clusters is a primary sign of breast cancer; however, it is difficult and time consuming for radiologists to classify microcalcifications as malignant or benign. In this paper, a novel method for the classification of microcalcification clusters in mammograms is proposed. METHODS: The topology/connectivity of individual microcalcifications is analyzed within a cluster using multiscale morphology. This is distinct from existing approaches that tend to concentrate on the morphology of individual microcalcifications and/or global (statistical) cluster features. A set of microcalcification graphs are generated to represent the topological structure of microcalcification clusters at different scales. Subsequently, graph theoretical features are extracted, which constitute the topological feature space for modeling and classifying microcalcification clusters. k-nearest-neighbors-based classifiers are employed for classifying microcalcification clusters. RESULTS: The validity of the proposed method is evaluated using two well-known digitized datasets (MIAS and DDSM) and a full-field digital dataset. High classification accuracies (up to 96%) and good ROC results (area under the ROC curve up to 0.96) are achieved. A full comparison with related publications is provided, which includes a direct comparison. CONCLUSION: The results indicate that the proposed approach is able to outperform the current state-of-the-art methods. Significance: This study shows that topology modeling is an important tool for microcalcification analysis not only because of the improved classification accuracy but also because the topological measures can be linked to clinical understanding.

Myofibre segmentation in H&E stained adult skeletal muscle images using coherence-enhancing diffusion filtering.

BACKGROUND: The correct segmentation of myofibres in histological muscle biopsy images is a critical step in the automatic analysis process. Errors occurring as a result of incorrect segmentations have a compounding effect on latter morphometric analysis and as such it is vital that the fibres are correctly segmented. This paper presents a new automatic approach to myofibre segmentation in H&E stained adult skeletal muscle images that is based on Coherence-Enhancing Diffusion filtering. METHODS: The procedure can be broadly divided into four steps: 1) pre-processing of the images to extract only the eosinophilic structures, 2) performing of Coherence-Enhancing Diffusion filtering to enhance the myofibre boundaries whilst smoothing the interior regions, 3) morphological filtering to connect unconnected boundary regions and remove noise, and 4) marker controlled watershed transform to split touching fibres. RESULTS: The method has been tested on a set of adult cases with a total of 2,832 fibres. Evaluation was done in terms of segmentation accuracy and other clinical metrics. CONCLUSIONS: The results show that the proposed approach achieves a segmentation accuracy of 89% which is a significant improvement over existing methods.