Protein subcellular localization of fluorescence microscopy images: Employing new statistical and Texton based image features and SVM based ensemble classification

• Prediction system “IEH-GT” for protein localization in fluorescence microscopy images.
• Utilizing information about the patterns of intensity-variations of proteins.
• New feature extraction based on individual features along various orientations.
• Discriminative learning through individual feature spaces of both GLCMs and textons.
• Employing the learning capabilities of ensemble using SVM and majority voting.

Image based subcellular localization of proteins is a challenging pattern recognition task and is of prime importance in understanding different functions of proteins. Extracting information about the protein's subcellular location using discriminative feature extraction strategies helps in understanding the behavior of different proteins under different circumstances.In this work, an intelligent prediction system for protein subcellular localization using fluorescence microscopy images is developed. The proposed prediction system IEH-GT uses a new feature extraction strategy and ensemble classification. The new feature extraction mechanism exploits statistical and Texton based image descriptors, whereas ensemble classification is performed using the majority voting based ensemble of SVMs. The novelty of IEH-GT prediction system lies (a) in the individual exploitation of the feature spaces generated from both individual Gray Level Co-occurrence Matrices (GLCMs) and Texton images as well as (b) in the manner the extracted features are exploited using the learning capabilities of SVM, which is utilized as base classifier in majority voting based ensemble. In this work, the effect of the individual orientations of GLCMs and Texton images on the capability of protein-recognition is investigated.It is observed that GLCMs along different orientations characterize different information regarding intensity distribution and relative positions of pixels in a certain neighborhood. Similarly, the individually generated features from each Texton image help in reducing the overlapping information. The proposed IEH-GT prediction system is thus able to effectively exploit information about the patterns of intensity-variations of proteins. It is experimentally observed that due to the capability of the proposed IEH-GT system in differentiating diverse surface structures of proteins, the mis-predictions of similar proteins such as Golgpp and Golgia are substantially reduced.