Biomedical subjects

S T C Wong

Publications and source records attributed to S T C Wong.

3 recordsLinked to original sources

PubMed2005

Limitations of Wada memory asymmetry as a predictor of outcomes after temporal lobectomy.

BACKGROUND: The intracarotid amobarbital (Wada) test can be used to evaluate hemispheric memory capacity before anterior temporal lobectomy (ATL). Most patients demonstrate better memory with injection ipsilateral to planned resection (expected asymmetry [EA]), but a substantial minority show better memory with contralateral injection (unexpected asymmetry [UA]). Both degree and direction of Wada memory asymmetry (WMA) have been associated with worse surgical outcome in small series. Reports also suggest that UA is associated with greater decline in verbal memory after left ATL (L-ATL). METHODS: The relationship between WMA and surgical outcome (at 3 months, 1 year, and last follow-up) was examined in a large group of ATL patients (108 L, 119 R) with both EA and UA. Also, memory in a subgroup (96 L, 108 R) was examined, comparing subscores of the Rey Auditory Verbal Learning Test obtained preoperatively, at 3 months, and at 1 year. RESULTS: Thirty-six percent of L-ATL and 8% of R-ATL patients had UA. UA was associated with worse surgical outcome at 1 year for R-ATL patients but was not associated with worse outcome for L-ATL patients. There was no correlation between WMA and persistent postoperative verbal memory change for patients with L- or R-ATL. CONCLUSIONS: Unexpected asymmetry is uncommon in patients with right anterior temporal lobectomy (R-ATL) and may be a risk marker of poor surgical outcome. This relationship may be obscured by language confounds in patients with L-ATL. The results suggest that Wada asymmetry (using mixed stimuli) does not predict postoperative verbal memory; it is unclear whether this finding is generalizable to centers using only nonverbal stimuli.

Adolescent↗

PubMed2005

Image segmentation feature selection and pattern classification for mammographic microcalcifications.

Since microcalcifications in X-ray mammograms are the primary indicator of breast cancer, detection of microcalcifications is central to the development of an effective diagnostic system. This paper proposes a two-stage detection procedure. In the first stage, a data driven, closed form mathematical model is used to calculate the location and shape of suspected microcalcifications. When tested on the Nijmegen University Hospital (Netherlands) database, data analysis shows that the proposed model can effectively detect the occurrence of microcalcifications. The proposed mathematical model not only eliminates the need for system training, but also provides information on the borders of suspected microcalcifications for further feature extraction. In the second stage, 61 features are extracted for each suspected microcalcification, representing texture, the spatial domain and the spectral domain. From these features, a sequential forward search (SFS) algorithm selects the classification input vector, which consists of features sensitive only to microcalcifications. Two types of classifiers-a general regression neural network (GRNN) and a support vector machine (SVM)--are applied, and their classification performance is compared using the Az value of the Receiver Operating Characteristic curve. For all 61 features used as input vectors, the test data set yielded Az values of 97.01% for the SVM and 96.00% for the GRNN. With input features selected by SFS, the corresponding Az values were 98.00% for the SVM and 97.80% for the GRNN. The SVM outperformed the GRNN, whether or not the input vectors first underwent SFS feature selection. In both cases, feature selection dramatically reduced the dimension of the input vectors (82% for the SVM and 59% for the GRNN). Moreover, SFS feature selection improved the classification performance, increasing the Az value from 97.01 to 98.00% for the SVM and from 96.00 to 97.80% for the GRNN.

Breast Neoplasms↗

PubMed2002

De-noising of left ventricular myocardial borders in magnetic resonance images.

In short axis left ventricular MR images, endocardial borders are the major parameters in evaluation of cardiovascular functions such as end diastolic volume, end systolic volume, and ejection fraction. Functional analysis captures the dynamic behavior of the cardiovascular system as revealed by the movement of the endocardial borders over time. Because of the huge number of MR images, an effective computerized tool is required for real time applications. One of the widely used automatic border detection algorithm-dynamic programming-generates zigzag borderlines, which lead to measurement errors. This paper surveys the performance of the wavelet adaptive filter, the snake, and the medial filter in smoothing over the zigzag borders generated by dynamic programming. Statistical analysis of two hundred and sixty four images from sixteen subjects show that all three algorithms can reduce the border line errors in terms of Hausdorff distance and border area error; however, only the wavelet adaptive filter is effective in providing the physiological measurements such as ejection fraction, end systolic volume and end diastolic volume.

Algorithms↗