Automatic Identification of Bacterial Types using Statistical Image Modeling Sigal Trattner, Dr. Hayit Greenspan, Prof. Shimon Abboud Department of Biomedical Engineering Faculty of Engineering, Tel-Aviv University In collaboration with Dr. Gabi Teper Spring Diagnostics Ltd.

Bacteriophage-(phage) typing is a method used to identify bacterial types by determining the bacteria reactivity to a set of selected phages; currently it is performed manually. Bacteria are identified as the prime cause for disease outbreak. The identification of bacterial types enables us to find a suitable cure for the disease and to control it. Positive phage reaction Negative phage reaction Introduction

Objective of our study  Identify profiles of bacterial types automatically, using computer vision and statistical modeling techniques. *To date: Manual, subjective diagnosis of the results. * Technology is enabling the increase in the number of phages used for typing (via new technology of “ Spring Diagnostics ” ).  Spot Finding  Spot Categorization

Variance in levels of intensity Reaction shape is irregular Variance in contrast of images Non-uniformity of background across the image Dust and other contamination Data: Staphylococcus Aureus.

Algorithm Methodology Preprocessing Feature Extraction and Modeling Segmentation Set of images (a group) Phage Profile of bacterial type Preprocessing Segmentation Preprocessing Segmentation Grid Spot Finding Spot Categorization Phage Profiling

Feature Space GMM The distribution of a random variable is a mixture of K Gaussians if its density function is: Parameter set EM Statistical Modeling Tool (GMM & EM)

Given a set of feature vectors, the maximum likelihood estimation of the parameter set: The EM algorithm: iterative method to obtain increasing the likelihood in each iteration :

Image pixels (Intensity samples per image) Foreground & Background Pixel Intensity  Intensity samples of each image are modeled as a Gaussian mixture distribution.  Segmentation= Probabilistic affiliation of each pixel to background and foreground (signal). Segmentation EM GMM

Segmentation Each pixel is now affiliated with the most probable Gaussian cluster. Probability of x to be labeled as foreground or background (1 or 0): Pixel labeling : Intensity 0 or 1

Spot Finding Original image Preprocessed image Signal Background GMM generated for the pixel intensity distribution per image

Segmented image De-rotated image and grid alignment Spot Finding

Spot Categorization Image Spots (data=all images in group ) Feature Vectors (NA, SI) Probabilistic Categorization to + / - EM GMM  Normalized Area (NA):  Shape Index (SI ): Features A=area of signal T= area threshold P=Perimeter of signal

GMM has clearly separated two main modules, using the chosen features. NE SI + - Spot Categorization

Probability Category

% Correlation between supervised spot categorization and automatic spot categorization Spot Categorization Results - + M M + Auto +

From Spots to Bacterial Type Profiling : Average Probability of phage G to +/- j Spots related to the same phage G Phage profiling

Phage Profile Phage no. 19 Bacterial Type Profiling

Group 3 Group 4 Group 2 Group 1 Differentiation across profiles of different groups Data=4 image groups of approx 40 scanned images and 144 phages checked on bacteria of Staphylococcus Aureus

Profile Uniformity Similarity of phage profiles that are extracted from a single group Group 3 = 260 images and 144 phages checked

Results – Test Set II Group #1 : 328 images ; Group #2 : 72 images Image batch of group #1 Uniformity 90% Algorithm Image batch of group #2 Uniformity 93% Algorithm Image batch of group #1 Uniformity 82% Algorithm Image batch of group #2 &

Conclusions An automated tool is presented for translating large sets of microbiological visual information into a probabilistic phage profile of a bacterial type. The tool is consistent, objective and robust. Statistical based decisions are used. The tool may be applied in different domains such as phage therapy, and the domain of cDNA Microarray analysis.

Thank You

Expectation step: estimate the Gaussian clusters to which the points in feature space belong Maximization step: maximum likelihood parameter estimates using this data