Govindan V K

Bio: Govindan V K is an academic researcher. The author has contributed to research in topics: Image segmentation & Fuzzy logic. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Bag-of-Features in Microscopic Images Classification

Abstract: Microscopic image analysis plays a foremost role for understanding biological processes, diagnosis of diseases and cells/ tissues identification. Microscopic image classification is one of the challenging tasks that have a leading role in the medical domain. In this chapter, an overview on different classification techniques elaborated with microscopic images is presented to guide the reader through the advanced knowledge of major quantitative image classification approaches. Applied examples are conducted to classify different Albino rats’ samples captured using light microscope for three different organs, namely hippocampus, renal and pancreas. The Bag-of-Features (BoF) technique was employed for features extraction and selection. The BoF selected features were used as input to the multiclass linear support vector machine classifier. The proposed classifier achieved 94.33% average classification accuracy for the three classes. Additionally, for binary classification the achieved average accuracy was 100% for hippocampus and pancreas sets classification.

Retinal and cancer cell image segmentation for predicting the diseased images

Abstract: Image segmentation in conventional learning approaches, the consumer applies only labeled or unlabelled training data set. The advanced application of segmentation in semi supervised learning to build better understanding of learners such a way that user could able to use both labeled data and un labeled data. In this research work focus to multi image model for semi supervised segmentation in retina and cancer cell images. The principal assets of the paper are that predicting the diseases diabetic and cancer with efficient training mechanism in a way that less human endeavor and higher correctness are achieved. We highlight the semi supervised segmentation in multi image model to classify diseased image or non diseased image.