In this paper, we propose a generalized Laplacian of Gaussian (LoG) (gLoG) filter for detecting general elliptical blob structures in images. The gLoG filter can not only accurately locate the blob centers but also estimate the scales, shapes, and orientations of the detected blobs. These functions can be realized by generalizing the common 3-D LoG scale-space blob detector to a 5-D gLoG scale-space one, where the five parameters are image-domain coordinates (x, y), scales (σ(x), σ(y)), and orientation (θ), respectively. Instead of searching the local extrema of the image's 5-D gLoG scale space for locating blobs, a more feasible solution is given by locating the local maxima of an intermediate map, which is obtained by aggregating the log-scale-normalized convolution responses of each individual gLoG filter. The proposed gLoG-based blob detector is applied to both biomedical images and natural ones such as general road-scene images. For the biomedical applications on pathological and fluorescent microscopic images, the gLoG blob detector can accurately detect the centers and estimate the sizes and orientations of cell nuclei. These centers are utilized as markers for a watershed-based touching-cell splitting method to split touching nuclei and counting cells in segmentation-free images. For the application on road images, the proposed detector can produce promising estimation of texture orientations, achieving an accurate texture-based road vanishing point detection method. The implementation of our method is quite straightforward due to a very small number of tunable parameters.

In this paper, we describe the design and development of a multi-tiered CBIR system for microscopic images utilizing a reference database that contains images of more than one disease. Proposed CBIR system uses a multi-tiered approach to classify and retrieve microscopic images involving their specific subtypes which are mostly difficult to discriminate and classify. This system enables both multi-image query and slide-level image retrieval in order to protect the semantic consistency among the retrieved images. New weighting terms, inspired from information retrieval (IR) theory, are defined for multiple-image query and retrieval. Performance of the system was tested on a dataset including 1666 imaged high power fields (HPF) extracted from 57 Follicular Lymphoma (FL) tissue slides with three subtypes and 44 Neuroblastoma (NB) tissue slides with four subtypes, where each slide is semantically annotated according to their subtypes by expert pathologists. By using leave-one-slide out testing scheme, the multi-image query algorithm with the proposed weighting strategy achieves about 93% and 86% of average classification accuracy at the first rank retrieval, outperforming the image-level retrieval accuracy by about 38 and 26 percentage points, for FL and NB diseases, respectively.