Blood Group Detection Using Image Processing Matlab Project with Source Code

ABSTRACT

           Determining of blood types is very important during emergency situation before administering a blood transfusion. Presently, these tests are performed manually by technicians, which can lead to human errors. Determination of the blood types in a short period of time and without human errors is very much essential. A method is developed based on processing of images acquired during the slide test. The image processing techniques such as thresholding and morphological operations are used. The images of the slide test are obtained from the pathological laboratory are processed and the occurrence of agglutination are evaluated. Thus the developed automated method determines the blood type using image processing techniques. The developed method is useful in emergency situation to determine the blood group without human error.

         Before the blood transfusion it is necessary to perform certain tests. One of these tests is the determination of blood type. There are certain emergency situations which due to the risk of patient life, it is necessary to administer blood immediately. The tests currently available require moving the laboratory, it may not be time enough to determine the blood type and is administered blood type O negative considered universal donor and therefore provides less risk of incompatibility. However, despite the risk of incompatibilities be less sometimes that cause death of the patient and it is essential to avoid them. Thus, the ideal would be to determine the blood type of the patient. Secondly, the pre-transfusion tests are performed by technicians, which lead to human errors. Since these human errors can translate into fatal consequences, being one of the most significant causes of fatal blood transfusions is important to automate the procedure of these tests. Various blood type classification, diffusive reflectance, ABO Rh-D blood typing using simple morphological image processing.There is a scope for determining blood types using image processing techniques. Image segmentation algorithm for blood type classification and various image processing parameters are analyzed. Image features, such as color, texture, shape are analyzed. Low quality ancient document images and antibody agent analysis using image processing is explained. The slide test consists of the mixture of one drop of blood and one drop of reagent, being the result interpreted according to the occurrence or not of agglutination. The combination of the occurrence and nonoccurrence of the agglutination determines the blood type of the patient. Thus, the software developed in image processing techniques allows, through an image captured after the procedure of the slide test detect the occurrence of agglutination and consequently the blood type of the patient.

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Mr. Roshan P. Helonde

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Matlab Project for Electronic Online Voting Machine (EVM) Using Matlab

ABSTRACT

                    Electronic voting machine is generally used now days in some countries including India for conducting election of government in a country. But the Electronic voting machine has certain disadvantages like illegal voting and insecurity. Hence the concept of online voting system is started in some countries for conducting election. Most of the developed countries have started using online voting system but they are facing some problems in conducting it. Estonia is the only country started conducting the online voting system in national election. But the percentage of voting is only 20% to 30%. Different researchers have designed a online voting system But the system are not so much efficient in terms of accuracy and security. Also the voting system has high error rate. Hence the voting system is not flexible and can be used for specific region only. Biometric authentication is found to be more secure and accurate in certain application. Different biometric authentications like fingerprint, retina etc. can be used in designing an application to enhance the security. As fingerprint of every individual is unique it can be used for designing a voting system. Different fingerprint matching techniques has been discussed considering the FRR ratio.

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Mr. Roshan P. Helonde

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Matlab Project with Source Code Contrast Enhancement using Adaptive Gamma Correction With Weighting Distribution Technique

ABSTRACT

                 One of the important techniques in digital image processing is to enhance images. Contrast enhancement is a method that is used to enhance images for viewing process or for further analysis of images. Main idea behind contrast enhancement techniques is to increase contrast and to preserve original brightness of images. In this paper a contrast enhancement technique is proposed that first segments histogram of image recursively and then applies Adaptive Gamma Correction with Weighting Distribution (AGCWD) Technique. The proposed technique is basically an improvement over AGCWD technique and aims to get better contrast enhancement and brightness preservation than AGCWD technique. The image enhancement is one of the significant techniques in digital image processing. It has an important role in various fields where images are to be understood and analyzed. Image enhancement is done on an image to improve its visual effects and quality or to make it more appropriate for further processing by another application. An image can have low contrast or bad quality due to a number of reasons like poor quality of imaging device, adverse external conditions at the time of image acquisition and many more. The contrast enhancement is one of the commonly used image enhancement method

                     Histogram equalization is the traditional technique for contrast enhancement. It basically maps gray levels based on probability distribution of input image. But image obtained by this method can produce undesirable effects in image and also original brightness of image is not preserved. Histogram equalization technique redistributes probability densities. Adaptive Gamma Correction with Weighting Distribution (AGCWD) technique is based on histogram modification method. This technique combines both gamma correction and histogram equalization techniques. Gamma correction is a transform based histogram modification technique that uses a varying parameter γ (gamma). Gamma correction method had problem that unvaried modification results for every image because a predefined value was used for all images. Histogram equalization had problem of under enhancement and over enhancement. So the AGCWD technique removed disadvantages of both gamma correction and Histogram Equalization techniques by combining both techniques and using a weighting function. In this technique gamma correction is applied using normalized cumulative density function (cdf). The AGCWD technique effectively enhances images. To further improve this technique to get better contrast enhancement and better brightness preservation an improvement is proposed in this project. Improvement proposed is based on recursive segmentation of histogram.

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Matlab Project with Source Code LSB based Audio Steganography for Enhancement in Security

ABSTRACT

                    Steganography is one of the best data hiding technique in the world which can be used to hide data without its presence felt. In today’s digital world most of us communicate via use of electronic media or internet. Most people among us remain unaware about the data loss or data theft which can happen on online transmission of data or message. Valuable information including personal data, messages transmitted through internet is vulnerable to hackers who may steal or decrypt our data or messages. This poject is about enhancing the data or message security with use of Audio Steganography using LSB algorithm to hide the message into multiple audio files. The message hidden by this application is less vulnerable to be stolen than other similar applications. This is due to following reasons: Firstly files are taken to hide high amount of message which enhance information hiding capacity. Secondly before being hidden, the message is broken into parts and shuffled randomly based on permutation generated at runtime so even if the LSB gets encountered the message is still unarranged and meaningless which enhances its security. This application is capable to carry large amount of information with greater security.

                     As world is changing fast, people wants to save their time and resources to keep pace with the fast growing technology for the fulfillment of their needs. As internet has become a working need of the people like electronic banking, mobile banking, online shopping, transferring data from one place to another, gathering or retrieving of information. Data Security need is also increasing due to risk of theft, hacker, intruders, eavesdroppers, sabotage and unauthorized user. Security can be achieved using cryptography which encrypts message and make it unreadable from unauthorized people or watermarking technique provides copyright protection and the third one is steganography. Steganography is a uniquetechnique coming from old times which help user to hide their critical information without creating any suspicion. Information hiding can be done in various cover mediums like image, audio, video, text etc. Cover is chosen according to the need like audio steganography is an interesting medium because latest song or famous songs can be used to hide messages. Embedding techniques are chosen according to requirement. Some of the techniques are LSB coding, parity coding, phasecoding, spread spectrum and echo hiding. It can be used for hiding any information like secret formulas, images,private communicationand forensic authentication. As audio steganography uses audio as a cover medium,similarly this application too uses an audio as a platform for hiding the message. User provides input message in the form of text and chooses the audio wave file to hide the message. This application provides a smart and interactive interface for message hiding and its retrieval. Message is shuffled in random sequencebefore being hidden. Random sequence which is generated based on certain factors is used to shuffle the message before hiding it. This further enhances the data security.

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Mr. Roshan P. Helonde

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Seam Carving Using Image Processing Matlab Project with Source Code

ABSTRACT

             Image Processing is an important technology for performing image operations. The analysis and manipulation on a digitized image helps to improve its quality. Image Processing offers a number of techniques to process an image such as Image Resizing, Image Enhancement etc. Image resizing is a key process for displaying visual media on different devices, and it has attracted much attention in the past few years. This paper defines preserving an important region of an image, minimizing distortions, and improving efficiency. Image Resizing can be more effectively reached with a better interpretation of image semantics. A new image importance map and a new seam criterion for image re-targeting is presented. Content-aware image resizing is a promising theme in computer vision and image processing. The seam carving method can effectively achieve image resizing which needs to define image importance to detect the salient context of images.

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Mr. Roshan P. Helonde

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Lung Cancer Detection in Medical Imaging Using Image Processing Matlab Project with Source Code

ABSTRACT

                   The most common cause of lung cancer is long‐term exposure to tobacco smoke, which causes 80‐90% of lung cancers. Cancer cells can be carried away from the lungs in blood, or lymph fluid that surrounds lung tissue. Lymph flows through lymphatic vessels, which drain into lymph nodes located in the lungs and in the center of the chest. Lung cancer often spreads toward the center of the chest because the natural flow of lymph out of the lungs is toward the center of the chest. As for the stages, in general there are four stages of lung cancer; I through IV. One of the major reason for non-accidental death is cancer. It has been proved that lung cancer is the topmost cause of cancer death in men and women worldwide. The death rate can be reduced if people go for early diagnosis so that suitable treatment can be administered by the clinicians within specified time. Cancer is, when a group of cells go irregular growth uncontrollably and lose balance to form malignant tumors which invades surrounding tissues. Cancer can be classified as Non-small cell lung cancer and small cell lung cancer. The various ways to detect lung cancer is by the use of image processing , pattern recognition and artificial neutral network to develop Computer aided diagnosis. In this project we use the techniques and algorithm used in image processing to detect cancer in three types of medical images. In this system first of all the medical images are recorded using a suitable imaging system. The images obtained are taken as input for the system where the image first go through the various steps of image processing like pre-processing, edge detection, morphological processing ,feature extraction.

                   Lung cancer which is among the five main types of cancer is a leading one to overall cancer mortality. Cancer is a serious health problem among various kinds of diseases. World Health Organization (WHO) reports that worldwide 7.6 million deaths are caused by cancer each year. Uncontrollable cell development in the tissues of the lung is called as lung cancer. Lung nodule is an abnormality that leads to lung cancer, characterized by a small round or oval shaped growth on the lung which appears as a white shadow in the CT scan. These uncontrollable cells restrict the growth of healthy lung tissues. If not treated, this growth can spread beyond the lung in the nearby tissue called metastasis and, form tumors. In order to preserve the life of the people who are suffered by the lung cancer disease, it should be pre‐diagnosed. The overall 5‐year survival rate for lung cancer patients increases from 14 to 49% if the disease is detected in time. So there is a need of pre-diagnosis system for lung cancer disease which should provide better results.

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Mr. Roshan P. Helonde

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Breast Cancer Detection Using Neural Networks Matlab Project with Source Code

ABSTRACT

                    Cancer is the major threat for human being health and its number of patients increasing word wide due to the global warming, even if there are new therapies and treatments proposed by research doctors, but level of cancer defines the ability of its cure. There are different types of cancers from which human being is suffering [male and female]. In this project we are focusing on breast cancer in women, rest allcancers are out of scope of this paper. Large number of women population is affected by the breast cancer. A different type of reasons causes the breast cancer such as X-Ray. For women’s, breast cancer is most common cancer, and it has been increasing since from last decade. The early detection of breast cancer helps to completely cure it through the treatment. The early detection is done by self-exam which can be done by woman in each month. This process is refereed as breast cancer early detection. However currently many hospitals and doctors uses the mammography test and resulted as effective technique for breast cancer early detection. The aim of this test is to perform early detection of breast cancer using characteristic masses detection as well as micro calcifications as these characteristics are considered as most important factor of breast cancer.

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Mr. Roshan P. Helonde

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Image Compression Using DCT and DWT Matlab Project with Source Code

ABSTRACT

                    Image compression means reducing the size of graphics file, without compromising on its quality. Depending on the reconstructed image, to be exactly same as the original or some unidentified loss may be incurred, two techniques for compression exist. Two techniques are: lossy techniques and lossless techniques. This project presents DWT and DCT implementation because these are the lossy techniques .This project aims at the compression using DCT and Wavelet transform by selecting proper method, better result for PSNR have been obtained.

                     Compression refers to reducing the quantity of data used to represent a file, image or video content without excessively reducing the quality of the original data. Image compression is the application of data compression on digital images. The main purpose of image compression is to reduce the redundancy and irrelevancy present in the image, so that it can be stored and transferred efficiently. The compressed image is represented by less number of bits compared to original. Hence, the required storage size will be reduced, consequently maximum images can be stored and it can transferred in faster way to save the time, transmission bandwidth. For this purpose many compression techniques i.e. scalar/vector quantization, differential encoding, predictive image coding, transform coding have been introduced. Among all these, transform coding is most efficient especially at low bit rate. Depending on the compression techniques the image can be reconstructed with and without perceptual loss. In lossless compression, the reconstructed image after compression is numerically identical to the original image. In lossy compression scheme, the reconstructed image contains degradation relative to the original. Lossy technique causes image quality degradation in each compression or decompression step. In general, lossy techniques provide for greater compression ratios than lossless techniques i.e. Lossless compression gives good quality of compressed images, but yields only less compression whereas the lossy compression techniques lead to loss of data with higher compression ratio. The approaches for lossy compression include lossy predictive coding and transform coding. Transform coding, which applies a Fourier-related transform such as DCT and Wavelet Transform such as DWT are the most commonly used approach. JPEG is the best choice for digitized photographs. The Joint Photographic Expert Group (JPEG) system, based on the Discrete Cosine Transform (DCT), has been the most widely used compression method. Discrete Cosine Transform (DCT) is an example of transform coding. The DCT coefficients are all real numbers unlike the Fourier Transform. The Inverse Discrete Cosine Transform (IDCT) can be used to retrieve the image from its transform representation. DCT is simple when JPEG used, for higher compression ratio the noticeable blocking artifacts across the block boundaries cannot be neglected. The DCT is fast. It can be quickly calculated and is best for images with smooth edges. Discrete wavelet transform (DWT) has gained widespread acceptance in signal processing and image compression. In this paper we made a comparative analysis of two transform coding techniques DCT and DWT based on different performance measure such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Compression Ratio (CR).

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Mr. Roshan P. Helonde

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Image Fusion Using Wavelet Transform and Combined DWT and PCA Matlab Project with Code

ABSTRACT

          Image fusion is the technique of merging several images from multi-modal sources with respective complementary information to form a new image, which carries all the common as well as complementary features of individual images. With the recent rapid developments in the domain of imaging technologies, multisensory systems have become a reality in wide fields such as remote sensing, medical imaging, machine vision and the military applications.

          Image fusion provides an effective way of reducing this increasing volume of information by extracting all the useful information from the source images. Image fusion creates new images that are more suitable for the purposes of human/machine perception, and for further image-processing tasks such as segmentation, object detection or target recognition in applications such as remote sensing and medical imaging. The overall objective is to improve the results by combining DWT with PCA and non-linear enhancement. The proposed algorithm is designed and implemented in MATLAB using image processing toolbox. The comparison has shown that the proposed algorithm provides a significant improvement over the existing fusion techniques.

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Mr. Roshan P. Helonde

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Content Based Image Retrieval Systems (CBIR) Using Improved SVM Technique Matlab Project with Code

ABSTRACT

          Content based image retrieval utilizes representations of features that are automatically extracted from the images themselves. All most all of the current CBIR systems allow for querying by example, a technique wherein an image (or part of an image) is selected by the user as the query. The system extracts the feature of the query image, searchesthe database for images with similar features, and exhibits relevant images to the user in order of similarity to the query. In this context, content includes among other features, perceptual properties such as texture, color, shape, and spatial relationships. Many CBIR systems have been developed that compare, analyze and retrieve images based on one or more of these features. Some systems have achieved various degrees of success by combining both content based and text based retrieval. In all cases, however, there has been no definitive conclusion as to what features provide the best retrieval. In this project we present a modified SVM technique to retrieve the images similar to the query image.

         The volume of digital information is growing at an exponential rate with the steady growth of computer power, increasing access to Internet and declining cost of storage devices. Hence to effectively manage the image information, it is imperative to advance automated image learning techniques. Unlike the traditional method of text based image retrieval in which the image search is based on textual description associated with the images, Content Based Image Retrieval Systems (CBIR) retrieve image information based on the content of the image. These systems retrieve images that are semantically related to the user’s query by extracting visual contents of the image such as colour, texture, shape or any other information that can be automatically extracted from the image itself and using it as a criterion to retrieve content related images from the database. The retrieved images are then ranked according to there relevance between the query image and images in the database in proportion to a similarity measure calculated from the features .

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Mr. Roshan P. Helonde

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