CSEIT183116 | Received : 01 Jan 2018 | Accepted : 12 Jan 2018 | January-February-2018 [(3) 1 : 97-101 ] International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2018 IJSRCSEIT | Volume 3 | Issue 1 | ISSN : 2456-3307 97 Image Processing Based Bacterial Colony Counter Bhavika Jagga * , Dr. Dilbag Singh Department of Computer Science and Applications, Chaudhary Devi Lal University, Sirsa, Haryana, India ABSTRACT Enumeration of Bacterial Colonies is required in many fields such as in clinical diagnosis, biomedical research for prevention of harmful diseases and pharmaceutical industry to avoid contamination of products. Existing Bacterial Colony counter systems count Bacterial Colony manually which is a time consuming, less efficient and tedious process. Hence, automation for counting of bacterial colony was required. The proposed method count these colonies automatically using image processing techniques. This method will provide a greater degree of accuracy in counting of bacterial colonies. Proposed technique takes an image of bacterial colony and converts it into grayscale. Otsu thresholding is applied for segmentation of the image further its conversion into binary image. After that, morphological operations are applied to clean up the image by removing noise and unnecessary pixels. Distance and watershed transformations are applied on the binary image to create partitions among overlapped and joint bacteria. Region properties and labeling information of segmented image is used for counting of bacterial colony. Keywords: Bacterial Colony, Thresholding, Morphology, Distance Transform and Watershed Segmentation. I. INTRODUCTION Bacterial Colony is defined as a cluster of bacteria derived from one common bacterium. Microbiologists require accurate measure of the bacterial colonies for many biological procedures [1]. Enumeration of Bacterial Colonies is important for obtaining precise assessment of pathogens. Manual counting of bacterial colonies is a tedious and time consuming process. Automation of the process of counting bacterial colonies will save time and labor required for counting of colonies [2]. Image segmentation techniques are used to automate this counting process. Image segmentation changes the image into a form suitable for image analysis. In image segmentation, a digital image is divided into multiple segments. Image segmentation assigns label to each pixel of an image on the basis of visual characteristics. Image segmentation is very used in many areas such as object detection, image retrieval, object-based counting, tissue identification, cell counting and object tracking [3]. Watershed segmentation is widely used for counting microorganisms. It is classified as region-based segmentation approach. It is used to separate two touching objects or overlapping objects. It considers image as a topographic surface in which the gray level of each pixel is considered to be the height on the surface and the two overlapping or touching objects as catchment basins. The motive is to find out watershed ridge lines separating the two catchment basins for separating the two overlapping objects [4] [5]. II. LITERATURE REVIEW Sethi and Yadav (2012) used the multi- threshold segmentation procedures to count bacterial colonies for separating and detecting the colonies present. Final processed image is used for counting of separated colonies using a conventional single-threshold segmentation procedure. Results depicted the low and medium density bacterial colonies. It have been observed that the proposed technique does not hold good for low contrast images and high density medium of bacterial colonies. In case of low contrast images of colonies gets distorted after thresholding, leading to appearance of high curvature points along the boundary. These high curvature points get accumulated in count result [1].