Vector quantization (VQ) and Markov modeling methods for cellular phase classification using time-lapse fluorescence microscopic image sequences have been proposed in our previous work. However the VQ method is not always effective because cell features are treated equally although their importance may not be the same. We propose a subspace VQ method to overcome this drawback. The proposed method can automatically weight cell features based on their importance in modeling. Two weighting algorithms based on fuzzy c-means and fuzzy entropy clustering are proposed. Experimental results show that the proposed method can improve the cell phase classification rates.
|Title of host publication||Lecture Notes in Computer Science - Image Analysis and Recognition|
|Editors||Aurelio Campilho, Mohamed Kamel|
|Place of Publication||Germany|
|Number of pages||10|
|Publication status||Published - 2008|
|Event||5th International Conference, ICIAR 2008 - , Portugal|
Duration: 25 Jun 2008 → …
|Conference||5th International Conference, ICIAR 2008|
|Period||25/06/08 → …|
Tran, D., Pham, T., & Zhou, X. (2008). Subspace Vector Quantization and Markov Modeling for cell Phase Classification. In A. Campilho, & M. Kamel (Eds.), Lecture Notes in Computer Science - Image Analysis and Recognition (Vol. 5112, pp. 844-853). Germany: Springer.