Image analysis represents an invaluable tool in processing and analyzing biological data in an expeditious and reliable way. This paper describes the design and implementation of an image analysis system for the automatic assessment of the growth of Heliothiszea insects from color images. Specifically, the Heliothis zea is a corn earworm eating corn crops. Biotech researchers are interested in developing insecticidal bio-toxins with the best performance to kill or stunt the growth of the pest. Currently, assessing the effectiveness of different bio-toxin solutions is done mostly manually by biotech experts. The goal of this study is to investigate the use of image analysis for automating and improving the efficiency of this process. In this context, we have developed a prototype system for assessing insect growth from color images which contains three main stages: (1) insect segmentation from background, (2) region processing and feature extraction, and (3) categorization of insect growth. A probabilistic model based on mixtures of Gaussians has been adopted to segment the insect images. Also, back-propagation neural networks have been trained to classify the instar stage and life stage. The proposed system has 1 December 21, 2010 15:49 WSPC/INSTRUCTION FILE ijtai˙submit 2 Tom Patten, Wenjing Li, George Bebis, and Muhammad Hussain been evaluated on a set of real images.

Image analysis represents an invaluable tool in processing and analyzing biological data in an expeditious and reliable way. This paper describes the design and implementation of an image analysis system for the automatic assessment of the growth of Heliothiszea insects from color images. Specifically, the Heliothis zea is a corn earworm eating corn crops. Biotech researchers are interested in developing insecticidal bio-toxins with the best performance to kill or stunt the growth of the pest. Currently, assessing the effectiveness of different bio-toxin solutions is done mostly manually by biotech experts. The goal of this study is to investigate the use of image analysis for automating and improving the efficiency of this process. In this context, we have developed a prototype system for assessing insect growth from color images which contains three main stages: (1) insect segmentation from background, (2) region processing and feature extraction, and (3) categorization of insect growth. A probabilistic model based on mixtures of Gaussians has been adopted to segment the insect images. Also, back-propagation neural networks have been trained to classify the instar stage and life stage. The proposed system has been evaluated on a set of real images.

The presence of noise or errors in the stated feature values of biomedical data can lead to incorrect prediction. We introduce a Bayesian Network-based Noise Correction framework named BN-NC. After data preprocessing, a Bayesian Network (BN) is learned to capture the feature dependencies. Using the BN to predict each feature in turn, BN-NC estimates a feature’s error rate as the deviation between its predicted and stated values in the training data, and allocates the appropriate uncertainty to its subsequent findings during prediction. BN-NC automatically generates a probabilistic rule to explain BN prediction on the class variable using the feature values in its Markov blanket, and this is reapplied as necessary to explain the noise correction on those features. Using three real-life benchmark biomedical data sets (on HIV-1 drug resistance prediction and leukemia subtype classification), we demonstrate that BN-NC (1) accurately detects the errors in biomedical feature values, (2) automatically corrects for the errors to maintain higher prediction accuracy over competing methods including Decision Trees, Naive Bayes and Support Vector Machines, and (3) generates probabilistic rules that concisely explain the prediction and noise correction decisions. In addition to achieving more robust biomedical prediction in the presence of feature noise, by highlighting erroneous features and explaining their corrections, BN-NC provides medical researchers with high utility insights to biomedical data not found in other methods. 1