Feminna Sheeba

Detection of Plasmodium Falciparum in Peripheral Blood Smear Images

Malaria is a mosquito-borne infectious disease caused by the parasite Plasmodium, which requires accurate and early diagnosis for effective containment. In order to diagnose malaria in a patient, timely detection of malaria parasites in blood smear images is vital. The traditional methods are time–consuming, tedious and the quality of detection is highly subjective to the individual who performs the analysis. These results can clearly be improved upon by using image processing techniques. The malaria parasite appears in four stages, namely the ring, trophozoite, schizont, and gametocyte. The ring and the gametocyte stage are the ones seen in a peripheral blood smear and hence detecting these two stages, would help in the accurate diagnosis of malaria. The proposed work aims at automating the analysis of the blood smear images using appropriate segmentation techniques, thereby detecting infected red blood cells as well as the gametocytes found in the blood.

Segmentation and reversible watermarking of peripheral blood smear images

This paper proposes an automated method for the segmentation of peripheral blood smear images, depicting the differential count of white blood cells. It also proposes a reversible fragile watermarking technique, in which the watermark-related information is embedded in the Region of Non-Interest (RONI) of the image. Image authenticity and integrity, is maintained by embedding the Input message along with the Hash Message Authentication Codes (HMAC) of the image and the message itself.

Segmentation of sputum smear images for detection of tuberculosis bacilli

Background Tuberculosis (TB) is a common and lethal infectious disease, which requires accurate and early diagnosis for effective containment. Essential for the diagnosis of pulmonary infection is the detection of the bacilli through the manual microscopic examination of ZN-stained sputum smear, which is a time-consuming, complex process necessitating at least 8-10 minutes per slide. Moreover, the quality of the detection is highly subjective to the individual who performs the analysis. These results can clearly be improved upon by using image processing techniques. The proposed work uses the segmentation techniques to automate the analysis of the sputum smear images and to detect the presence of tuberculosis bacilli in them.

Detection of Overlapping Tuberculosis Bacilli in Sputum Smear Images

Tuberculosis (TB) is a common and lethal infectious disease caused by a germ (bacterium) called Mycobacterium tuberculosis. Early diagnosis of the disease is one of the primary challenges in curtailing its spread and is a critical step in the TB-Control Program worldwide. Among the most common methods used in the diagnosis of TB is the manual microscopic examination of a ZN-stained sputum smear which is a time-consuming and error-prone process. The diagnosis crucially depends on the number of viable or dormant mycobacteria in the sputum, which are seen as red colored rod-shaped objects in the smear under a microscope. This also means that the mycobacteria have to be detected accurately in order to arrive at the correct count, the accuracy of which could be affected when there are overlapping bacilli in the images. The use of Image Analysis in the detection of the mycobacteria will introduce a paradigm shift. The proposed work identifies such overlapping mycobacteria and uses techniques to total them accurately, which is an extension of an earlier work focusing only on segmentation of the tiny organisms. Normal bacilli are just 2-4 micrometers in length and 0.2-0.5 um in width. All the organisms that fall above their average size or show a variation in the ratio of the major-to-minor axis are identified to be overlapping mycobacteria, which are then used for further analysis. The count of mycobacteria that overlap is computed by obtaining the branch points in the skeleton of the overlapping object. The dataset used in the research consisted of eighty images, which were tested using a prototype application that achieved a success rate of 70%.

Morphology Based Detection of Abnormal Red Blood Cells in Peripheral Blood Smear Images

Red blood cells are the most abundant type of blood cells in the human body, delivering oxygen to body tissues. The count of these vital cells is often the first step done in analyzing a patient’s pathological condition. Normal RBC’s are biconcave in shape with a central pale area, and any deviation in size, shape, volume, structure or color represents an abnormal cell. Such abnormalities are detected by viewing the blood-smear images through a microscope, a time consuming and error-prone method. This process can be automated by analyzing the individual cells in a peripheral blood smear image and segmenting the cells using appropriate segmentation techniques. The proposed study aims at Morphologybased detection of abnormal red blood cells in peripheral blood smear images, based on their size and shapes. Abnormalities such as Anisocytosis, Macrocytosis and Microcytosis are detected based on the size of the RBCs. Variations in the shape of RBCs couldindicate various abnormalities. Convex hull based detection of speculated RBCs, is carried out in Acanthrocytosis and Echinocytosis. The condition Eliptocytosis, where some of the RBCs turn elliptical is detected using Houghman Transform. In the abnormality called Rouleaux the RBCs appear as stack of coins, which are detected by applying a watershed algorithm to individual stacks and counting the number of cells in the stack. Sickle cell anemia is another common condition in people, where few RBCs are sickle or crescent shaped and this shape is determined using the roundness factor. Codocytes resemble a bull’s eye, and can be identified by examining if the segmented RBCs have rounded areas within the cell. Dacrocytes are tear drop RBCs, which can be detected by analyzing the extreme points of the cell. The experiment was conducted for fifty images and the success rate achieved was 80%.

Convex Hull Based Detection of Overlapping Red Blood Cells in Peripheral Blood Smear Images

The Segmentation of Red Blood Cells (RBCs) in blood smear images to obtain their count is often the first step in the diagnosis of various pathological conditions. Although several procedures have been devised for this task, a majority of them suffer from performance degradation due to the overlapping of cells. Various researches have been carried out to split these overlapping cells. The proposed paper aims at suggesting two algorithms to find the concavity points in the overlapping RBCs’ contours. In the first approach, the dip points are obtained by analyzing the concave regions, obtained by finding out the Euclidean distance of all points in the overlapping cell to their convex hull. In the second approach, dip point identification is based only on the convex hull of the overlapping cell. The contours of the concave regions are analyzed from the perspective of the centroid. These two strategies were compared with the approach used in an earlier work, which also addressed the splitting of overlapping RBCs, by identifying the dip points using curve fitting and smoothing of the contours. The two approaches proposed in this paper are quite efficient in terms of accuracy and the time taken to achieve results. The specificity of the first approach was 90% and that of the second approach was 94%, meaning that the two new methods are far more advanced than the earlier work for which the specificity was only 75%.

Medical Image Encryption Using Block-Based Scrambling and Discrete Wavelet Transform

In today’s age of manifold advances in the field of medical imaging, a significant amount of sensitive and personal information related to patients is being transmitted electronically via images. With the advent of e-Health and Telemedicine in the vast field of medicine, there is a need to guarantee the authenticity and validity of the images being exchanged. The much-need security of medical images imposes the conditions of confidentiality, reliability and availability, and these can be attained by various Image Authentication methods and one such authentication is Image Encryption. The proposed work aims at an Image Encryption technique, which is a combination of Tiling, Scrambling and Image Transformation and Encryption of the image. The proposed architecture for encryption and decryption of a medical image is using a symmetric key, which gives the size of the tiles and the hash code of the image. The encryption algorithm divides the image into tiles of arbitrary size, scramble them using a scrambling technique and transform the scrambled image using Discrete Wavelet Transform (DWT). The hash code in the key is used to find out if tampering has taken place during transmission of the medical image.

Splitting of Overlapping Cells in Peripheral Blood Smear Images by Concavity Analysis

The diagnosis of a patients pathological condition, through thei?źstudy of peripheral blood smear images, is a highly complicated process, the results of which require high levels of precision. In order to analyze the cells in the images individually, the cells can be segmented using appropriate automated segmentation techniques, therebyi?źavoiding the cumbersome and error-prone existing manual methods. A marker controlled watershed transform, which was used in the previous study is an efficient technique to segment the cells and split overlapping cells in the image. However this technique fails to split the overlapping cells that do not have higher gradient values in the overlapping area. The proposed work aims toi?źanalyze the concavity of the overlapping cells and split the clumped Red Blood Cells RBCs, as RBC segmentation is vital in diagnosing various pathological disorders and life-threatening diseases such as malaria. Splitting is done based on the number of dip points in the overlapping region using developed splitting algorithms. Successful splitting of overlapped RBCs help the count of the RBCs remain accurate during the search for possible pathological infections and disorders.

Segmentation of Peripheral Blood Smear Images Using Tissue-Like P Systems

The tissue-like P Systems, which are based on the methodology of cell and tissue behavior in a human body, are used in various areas of computation. Segmentation of medical images is one such area where these systems can be used to identify various details and objects in those images. It is a highly challenging process, especially when dealing with blood smear images, which have a very complex cell structure. In order to analyze each object individually and to avoid the cumbersome and error-prone existing manual methods, images can be segmented using appropriate automated segmentation techniques. The proposed work aims at segmenting the nuclei of the White Blood Cells (WBCs) of the peripheral blood smear images, using tissue-like P Systems, which can help to identify various pathological conditions. In the first approach, segmentation is color based. The second approach is intensity based. In the third approach, morphology is used to strengthen the findings from the results.