False positive reduction in urinary particle recognition
The identification of non-cell objects in biological images is not a trivial task largely due to the difficulty in describing their characteristics in recognition systems. In order to better reduce the false positive rate caused by the presence of non-cell particles, we propose a novel approach using a local jet context features scheme combined with a two-tier object classification system. The newly proposed feature scheme, namely local jet context feature, integrates part of global features with the local jet features. The scheme aims to effectively describe the particle characteristics that are invariant to shift and rotation, and hence help to retain the critical shape information. The proposed two-tier particle classification strategy consists of a pre-recognition stage first and later a further filtering phase. Using the local jet context features coupled with a multi-class SVM classifier, the pre-recognition stage intends to assign the particles to their corresponding classes as many as possible. To further reduce the false positive particles, next a decision tree classifier based on shape-centered features is applied. Our experimental study shows that through the proposed two-tier classification strategy, we are able to achieve 85% of identification accuracy and 80% of F1 value in urinary particle recognition. The experiment results demonstrate that the proposed local jet context features are capable to discriminate particles in terms of shape and texture characteristics. Overall, the two-tier classification stage is found to be effective in reducing the false positive rate caused by non-cell particles.
particle recognition svm segmentation feature identification