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Ming C Lin

Publications and source records attributed to Ming C Lin.

5 recordsLinked to original sources
PubMed2003

Usefulness of thallium-201 SPECT scintimammography to differentiate benign from malignant breast masses in mammographically dense breasts.

The purpose of this study was to assess the usefulness of thallium-201 (Tl-201) single-photon emission-computed tomography (SPECT) scintimammography to differentiate benign from malignant breast masses in 32 female Taiwanese patients with indeterminate mammographic probability of malignancy because of mammographically dense breasts. All breast masses were removed, and final histopathological diagnoses were obtained in all cases. The results showed that thallium-201 SPECT scintimammography findings were true-positive in 22 cases, false-positive in 1 case, true-negative in 7 cases, and false-negative in 2 cases. The diagnostic sensitivity, specificity, and accuracy were 91.7%, 87.5%, and 90.6%, respectively, for detecting breast cancer in mammographically dense breasts. In conclusion, thallium-201 SPECT scintimammography is a useful tool for differentiating benign from malignant breast masses in patients with indeterminate mammograms because of mammographically dense breasts.

Adult↗
PubMedDate not supplied

Incremental penetration depth estimation between convex polytopes using dual-space expansion.

We present a fast algorithm to estimate the penetration depth between convex polytopes in 3D. The algorithm incrementally seeks a "locally optimal solution" by walking on the surface of the Minkowski sums. The surface of the Minkowski sums is computed implicitly by constructing a local dual mapping on the Gauss map. We also present three heuristic techniques that are used to estimate the initial features used by the walking algorithm. We have implemented the algorithm and compared its performance with earlier approaches. In our experiments, the algorithm is able to estimate the penetration depth in about a milli-second on an 1 GHz Pentium PC. Moreover, its performance is almost independent of model complexity in environments with high coherence between successive instances.

Algorithms↗