Biomedical subjects

M Sonka

Publications and source records attributed to M Sonka.

24 records · Page 2Linked to original sources

PubMed1998

Ovarian ultrasound image analysis: follicle segmentation.

Ovarian ultrasound is an effective tool in infertility treatment. Repeated measurements of the size and shape of follicles over several days are the primary means of evaluation by physicians. Currently, follicle wall segmentation is achieved by manual tracing which is time consuming and susceptible to inter-operator variation. An automated method for follicle wall segmentation is reported that uses a four-step process based on watershed segmentation and knowledge-based graph search algorithm which utilizes priori information about follicle structure for inner and outer wall detection. The automated technique was tested on 36 ultrasonographic images of women's ovaries. Validation against manually traced borders has shown good correlation of manually defined and computer-determined area measurements (R2 = 0.85 - 0.96). The border positioning errors were small: 0.63+/-0.36 mm for inner border and 0.67+/-0.41 mm for outer border detection. The use of watershed segmentation and graph search methods facilitates fast, accurate inner and outer border detection with minimal user-interaction.

Algorithms↗

PubMed1998

Segmentation and interpretation of MR brain images: an improved active shape model.

This paper reports a novel method for fully automated segmentation that is based on description of shape and its variation using point distribution models (PDM's). An improvement of the active shape procedure introduced by Cootes and Taylor to find new examples of previously learned shapes using PDM's is presented. The new method for segmentation and interpretation of deep neuroanatomic structures such as thalamus, putamen, ventricular system, etc. incorporates a priori knowledge about shapes of the neuroanatomic structures to provide their robust segmentation and labeling in magnetic resonance (MR) brain images. The method was trained in eight MR brain images and tested in 19 brain images by comparison to observer-defined independent standards. Neuroanatomic structures in all testing images were successfully identified. Computer-identified and observer-defined neuroanatomic structures agreed well. The average labeling error was 7%+/-3%. Border positioning errors were quite small, with the average border positioning error of 0.8+/-0.1 pixels in 256 x 256 MR images. The presented method was specifically developed for segmentation of neuroanatomic structures in MR brain images. However, it is generally applicable to virtually any task involving deformable shape analysis.

Algorithms↗

PubMed1997

Towards a geometrically correct 3-D reconstruction of tortuous coronary arteries based on biplane angiography and intravascular ultrasound.

At present, 3-D reconstructions of coronary vessels are generated from intravascular ultrasound (IVUS) by stacking up ECG-gated segmented IVUS frames of a pullback sequence. This simplified approach always results in straight vessel reconstructions and, therefore, gives an incorrect representation of tortuous coronary arteries. A more realistic reconstruction of tortuous vessels may be obtained by data fusion with biplane angiography. The 3-D course of the vessel is first derived from the angiograms and then combined with the segmented IVUS images. In this paper, we focus on two problems associated with the data fusion method: The definition of the pullback path and the estimation of the IVUS catheter twist during pullback. A robust algorithm for calculation of tortuosity-induced catheter twist is reported that is based on sequential triangulation of the 3-D pullback path. The method is analyzed with computer simulations and validated in helical vessel phantoms. A largely automated data fusion approach is proposed and applied to tortuous coronary arteries in cadaveric pig hearts.

Animals↗

PubMed1997

Adaptive approach to accurate analysis of small-diameter vessels in cineangiograms.

In coronary vessels smaller than 1 mm in diameter, it is difficult to accurately identify lumen borders using existing border detection techniques. Computer-detected diameters of small coronary vessels are often severely overestimated due to the influence of the imaging system point spread function and the use of an edge operator designed for a broad range of vessel sizes, Computer-detected diameters may be corrected if a calibration curve for the X-ray system is available. Unfortunately, the performance of this postprocessing diameter correction approach is severely limited by the presence of image noise. We report here a new approach that uses a two-stage adaption of edge operator parameters to optimally match the edge operator to the local lumen diameter. In the first stage, approximate lumen diameters are detected using a single edge operator in a half-resolution image. Depending on the approximate lumen size, one of three edge operators is selected for the second full-resolution stage in which left and right coronary borders are simultaneously identified. The method was tested in a set of 72 segments of nine angiographic phantom vessels with diameters ranging from 0.46 to 4.14 mm and in 82 clinical coronary angiograms. Performance of the adaptive simultaneous border detection method was compared to that of a conventional border detection method and to that of a postprocessing diameter correction border detection method. Adaptive border detection yielded significantly improved accuracy in small phantom vessels and across all vessel sizes in comparison to the conventional and postprocessing diameter correction methods (p < 0.001 in all cases). Adaptive simultaneous coronary border detection provides both accurate and robust quantitative analysis of coronary vessels of all sizes.

Algorithms↗

PubMed1997

Quantification of pulmonary emphysema from lung computed tomography images.

A texture-based adaptive multiple feature method (AMFM) for evaluating pulmonary parenchyma from computed tomography (CT) images is described. This method incorporates multiple statistical and fractal texture features. The AMFM was compared to two previously published methods, namely, mean lung density (MLD) and the lowest fifth percentile of the histogram (HIST). First, the ability of these methods to detect subtle differences in ventral-dorsal lung density gradient in the prone normal lung was studied. Second, their abilities to differentiate between normal and emphysematous whole lung slices were compared. Finally, regional analyses comparing normal and emphysematous regions were performed by dividing the lungs. In the CT slices into six equal regions, ventral to dorsal, and analyzing each region separately. The results demonstrated that the AMFM could separate the ventral from the dorsal one-third of the normal prone lung with 89.8% accuracy, compared to an accuracy of 74.6% with the MLD and 64.4% with the HIST methods. The normal and emphysematous slices were separated on a global basis with 100.0% accuracy using the AMFM as compared to an accuracy of 94.7% and 97.4% using the MLD and HIST methods, respectively. The regional normal and emphysematous tissues were discriminated with an average accuracy of 97.9%, 89.9%, and 99.1% with the AMFM, MLD, and HIST methods, respectively. The three methods and the pulmonary function tests in the normal and emphysema groups were poorly correlated. Quantitative texture analysis using adaptive multiple features holds promise for the objective noninvasive evaluation of the pulmonary parenchyma.

Humans↗

PubMed1994

Lumen centerline detection in complex coronary angiograms.

We have developed a method for lumen centerline detection in individual coronary segments that is based on simultaneous detection of the approximate positions of the left and right coronary borders. This approach emulates that of a clinician who visually identifies the lumen centerline as the midline between the simultaneously-determined left and right borders of the vessel segment of interest. Our lumen centerline detection algorithm and two conventional centerline detection methods were compared to carefully-defined observer-identified centerlines in 89 complex coronary images. Computer-detected and observer-defined centerlines were objectively compared using five indices of centerline position and orientation. The quality of centerlines obtained with the new simultaneous border identification approach and the two conventional centerline detection methods was also subjectively assessed by an experienced cardiologist who was unaware of the analysis method. Our centerline detection method yielded accurate centerlines in the 89 complex images. Moreover, our method outperformed the two conventional methods as judged by all five objective parameters (p < 0.001 for each parameter) and by the subjective assessment of centerline quality (p < 0.001). Automated detection of lumen centerlines based on simultaneous detection of both coronary borders provides improved accuracy in complex coronary arteriograms.

Algorithms↗