Biomedical subjects

H D Tagare

Publications and source records attributed to H D Tagare.

6 recordsLinked to original sources

PubMed1999

Shape-based nonrigid correspondence with application to heart motion analysis.

A common problem in many biomedical imaging studies is that of finding a correspondence between two plane curves which aligns their shapes. A mathematical formulation and solutions to this problem is proposed in this paper. The formulation exhibits desirable properties. It allows for one-to-one as well as non-one-to-one correspondences, it consistently compares shape, even in nonrigid situations, and it is completely symmetric with respect to the two curves. A numerical implementation of the algorithm for finding the optimal correspondence is also reported. The algorithm is used to estimate nonrigid motion of the endocardium in MRI image sequences of normal and post-infarct dog hearts. The return error (the difference between the starting and ending positions of a point) is used as a performance measure to evaluate the technique. Since heart motion is periodic, the return error is a measure of consistency of the algorithm. Preliminary applications to other data sets are reported as well.

Algorithms↗

PubMed1997

Deformable 2-D template matching using orthogonal curves.

In this paper a new formulation of the two-dimensional (2-D) deformable template matching problem is proposed. It uses a lower-dimensional search space than conventional methods by precomputing extensions of the deformable template along orthogonal curves. The reduction in search space allows the use of dynamic programming to obtain globally optimal solutions and reduces the sensitivity of the algorithm to initial placement of the template. Further, the technique guarantees that the result is a curve which does not collapse to a point in the absence of strong image gradients and is always nonself intersecting. Examples of the use of the technique on real-world images and in simulations at low signal-to-noise ratios (SNR's) are also provided.

Algorithms↗

PubMedDate not supplied

Location and geometric description of carpal bones in CT images.

The carpal regions of ten cadaver extremities were imaged by CT. The images were combined into a 3-dimensional model of the carpus using a technique based on a dynamic programming algorithm to find an optimal estimate of the location of the bone boundaries in the CT images. The resulting set of surface points on each bone was used to compute volumes and principal and antipodal axes for the bones. A spatial coordinate system was established based on the positions of the centroids of three bones in the distal carpal row. The angular orientations of all carpal bones were determined with respect to this system. The principal axes for the same bone among ten wrist specimens proved to be more widely dispersed than the antipodal axes for the same bones. The antipodal axes also correspond more closely to an intuitive notion of the "longest axis" of the bones. We conclude that the antipodal axis is a more reliable and useful measure of bone orientation than the principal axis.

Adult↗

PubMedDate not supplied

Medical image collection indexing: shape-based retrieval using KD-trees.

The capacity to retrieve images containing objects with shapes similar to a query shape is desirable in medical image databases. We propose a similarity measure and an indexing mechanism for non-rigid comparison of shape which adds this capability to image databases. The (dis-)similarity measure is based on the observations that: (1) the geometry of the same organ in different subjects is not related by a strictly rigid transformation; and (2) the orientation of the organ plays a key role in comparing shape. We propose a similarity measure that computes a non-rigid mapping between curves and uses this mapping to compare oriented shape. We also show how KD-trees can index curves so that retrieval with our similarity measure is efficient. Experiments with real-world data from a database of magnetic resonance images are provided.

Abstracting and Indexing↗

PubMedDate not supplied

Medical image databases: a content-based retrieval approach.

Information contained in medical images differs considerably from that residing in alphanumeric format. The difference can be attributed to four characteristics: (1) the semantics of medical knowledge extractable from images is imprecise; (2) image information contains form and spatial data, which are not expressible in conventional language; (3) a large part of image information is geometric; (4) diagnostic inferences derived from images rest on an incomplete, continuously evolving model of normality. This paper explores the differentiating characteristics of text versus images and their impact on design of a medical image database intended to allow content-based indexing and retrieval. One strategy for implementing medical image databases is presented, which employs object-oriented iconic queries, semantics by association with prototypes, and a generic schema.

Abstracting and Indexing↗

PubMedDate not supplied

PathMaster: content-based cell image retrieval using automated feature extraction.

OBJECTIVE: Currently, when cytopathology images are archived, they are typically stored with a limited text-based description of their content. Such a description inherently fails to quantify the properties of an image and refers to an extremely small fraction of its information content. This paper describes a method for automatically indexing images of individual cells and their associated diagnoses by computationally derived cell descriptors. This methodology may serve to better index data contained in digital image databases, thereby enabling cytologists and pathologists to cross-reference cells of unknown etiology or nature. DESIGN: The indexing method, implemented in a program called PathMaster, uses a series of computer-based feature extraction routines. Descriptors of individual cell characteristics generated by these routines are employed as indexes of cell morphology, texture, color, and spatial orientation. MEASUREMENTS: The indexing fidelity of the program was tested after populating its database with images of 152 lymphocytes/lymphoma cells captured from lymph node touch preparations stained with hematoxylin and eosin. Images of "unknown" lymphoid cells, previously unprocessed, were then submitted for feature extraction and diagnostic cross-referencing analysis. RESULTS: PathMaster listed the correct diagnosis as its first differential in 94 percent of recognition trials. In the remaining 6 percent of trials, PathMaster listed the correct diagnosis within the first three "differentials." CONCLUSION: PathMaster is a pilot cell image indexing program/search engine that creates an indexed reference of images. Use of such a reference may provide assistance in the diagnostic/prognostic process by furnishing a prioritized list of possible identifications for a cell of uncertain etiology.

Abstracting and Indexing↗