Image Fusion Using PCA on MRI and CT Image Full Matlab Project Code

ABSTRACT

           Image Fusion is a procedure of merging the required image information from two or more images of the same scene or background into a single picture and the resultant fused picture will be more informational and complete than any of the input images, which was taken for fusion. Processing image could be multi sensor, multi-modal, multi-focal or multi passing. Image fusion is a strategy that combine complimentary details from two or more input image such that the new image gives more information and more suitable for the motivation behind human visual system. This project presents a survey on a percentage of the image fusion technique (simple average, simple minimum, simple maxima, PCA, DWT). Comparative examination of the considerable number of systems closes the better approach for its future exploration.

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Mr. Roshan P. Helonde

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Emotion Recognition from Audio Signal Full Matlab Project Code

ABSTRACT

          Emotion Recognition is a recent research topic in the field of Human Computer Interaction Intelligence and mostly used to develop wide range of applications such as stress management for call centre employee, and learning & gaming software, In E-learning field, identifying students emotion timely and making appropriate treatment can enhance the quality of teaching. Main aim of HCI is to achieve a more natural interaction between machine and humans. HCI is an emerging field using which we can improve the interactions between users and computers by making computers more respond able to the user’s needs. Today’s HCI system has been developed to identify who is speaking or what he/she is speaking. If in the HCI system, the computers are given an ability to detect human emotions then they can know how he/she is speaking and can respond accurately and naturally like humans do. In this project methodology for emotion recognition from speech signal is presented. Here, some of acoustic features are extracted from speech signal to analyze the characteristics and behavior of speech. The system is used to recognize the basic emotions. It can serve as a basis for further designing an application for human like interaction with machines through natural language processing and improving the efficiency of emotion. In this format, energy, Mel Frequency Cepstral Coefficients (MFCC) has been used for feature extraction from the speech signal. Support Vector Machine (SVM) are used for recognition of emotional states. English datasets are used for analysis of emotions with SVM Kernel functions.

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Mr. Roshan P. Helonde

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Detection of Anemia Diseases Present in Blood Cells Smear Using Image Processing Full Matlab Project

ABSTRACT

          Anemia is a blood disorder which results from the abnormalities of red blood cells and shortens the life expectancy to 42 and 48 years for males and females respectively. It also causes pain jaundice, shortness of breath, etc. Anemia is characterized by the presence of abnormal cells like sickle cell, ovalocyte, anisopoikilocyte. Sickle cell disease usually presenting in childhood, occurs more commonly in people from parts of tropical and subtropical regions where malaria is or was very common. A healthy RBC is usually round in shape. But sometimes it changes its shape to form a sickle cell structure; this is called as sickling of RBC. Majority of the sickle cells (whose shape is like crescent moon) found are due to low haemoglobin content. An image processing algorithm to automate the diagnosis of sickle-cells present in thin blood smears is developed. Images are acquired using a charge-coupled device camera connected to a light microscope. Clustering based segmentation techniques are used to identify erythrocytes (red blood cells) and Sickle-cells present on microscopic slides. Image features based on colour, texture and the geometry of the cells are generated, as well as features that make use of a priori knowledge of the classification problem and mimic features used by human technicians. The proposed image processing based identification of sickle-cells in anemic patient will be very helpful for automatic, sleek and effective diagnosis of the disease.

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Mr. Roshan P. Helonde

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Matlab Project for Plant Disease Detection & Classification using Image Processing Full Source Code

ABSTRACT

                  Diseases decrease the productivity of plant. Which restrict the growth of plant and quality and quantity of plant also reduces. Image processing is best way for detecting and diagnosis the diseases. In which initially the infected region is found then different features are extracted such as color, texture and shape. Finally classification technique is used for detecting the diseases. There are different feature extraction techniques for extracting the color, texture and edge features such as color space, color histogram, grey level co-occurrence matrix (CCM), Gabor filter, Canny and Sobel edge detector. India is agricultural country and most of population depends on agriculture. Farmers have wide range of selection in Fruit and Vegetable crops. The cultivation can be improved by technological support. Disease is caused by pathogen in plant at any environmental condition. In most of the cases diseases are seen on the leaves, fruits and stems of the plant, therefore detection of disease plays an important role in successful cultivation of crops. In most of cases plant diseases are caused by pathogens, microorganism, fungi, bacteria, viruses, etc. Sometimes unhealthy environment

include soil and water is also responsible for diseases in plants.

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Mr. Roshan P. Helonde

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Blood Group Detection and Classification Using Image Processing Matlab Project 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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Skin Disease Detection Using Image Processing Matlab Project Code IEEE Based Project

ABSTRACT

                Many of the skin diseases are very dangerous, particularly if not treated at an early stage. Skin diseases are becoming common because of the increasing pollution. Skin diseases tend to pass from one person to another. Human habits tend to assume that some skin diseases are not serious problems. Sometimes, most of the people try to treat these infections of the skin using their own method. However, if these treatments are not suitable for that particular skin problem then it would make it worse. And also sometimes they may not be aware of the dangerous of their skin diseases, for instance skin cancers. With advance of medical imaging technologies, the acquired data information is getting so rich toward beyond the human’s capability of visual recognition and efficient use for clinical assessment. In this project we propose a diagnosis system which will enable users to detect and recognize skin diseases with the help of image processing and provide the user advises or treatments based on the results obtained in a shorter time period than the existing methods. In this project, we will be constructing a diagnosis system based on the techniques of Image Processing. We will be making use of Matlab to perform the pre-processing and processing of the skin images of the users. This processing will be conducted on the different skin patterns and will be analyzed to obtain the results from which we can identify which skin disease the user is suffering from. This data will help in early detection of the skin diseases and in providing their cure. Through this we will be finding a cost effective and feasible test method for the detection of skin disorders. The results obtained will be classified according to the given prototype and diagnosis accuracy assessment will be performed to provide users with efficient and fast results.

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Mr. Roshan P. Helonde

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Lung Cancer Detection Using Image Processing Matlab Project 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. 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

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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