Lung Cancer Detection using Image Processing Matlab Project Source Code

ABSTRACT

        Lung cancer prevalence is one of the highest of cancers, at 18 %. One of the first steps in lung cancer diagnosis is sampling of lung tissues or biopsy. These tissue samples are then microscopically analyzed. This procedure is taken once imaging tests indicate the presence of cancer cells in the chest. Lung cancer diagnosis using lung images. One of them is that doctor still relies on subjective visual observation. A medical specialist must do thorough observation and accurate analysis in detecting lung cancer in patients. Hence, there is need for a system that is capable for detecting lung cancer automatically from microscopic images of biopsy. This method will improve the accuracy and efficiency for lung cancer detection. The aim of this research is to design a lung cancer detection system based on analysis of microscopic image of biopsy using digital image processing. Microscopic images of biopsy are feature extracted and classified. This method is implemented to detection of lung cancer of lung samples.

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Mr. Roshan P. Helonde

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Matlab Code for Blood Type Detection Using Image Processing

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 Pre-processing, Segmentation, Thresholding, Morphological operations and Support Vector Machine 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. 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 non occurrence of the agglutination determines the blood type of the patient. 

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Mr. Roshan P. Helonde

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Matlab code for Image Fusion using Wavelet Transform

ABSTRACT

            Different medical imaging techniques such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) provide different perspectives for the human body that are important in the physical disorders or diagnosis of diseases .To derive useful information from multimodality medical image data medical image fusion has been used. In the medical field different radiometric scanning techniques can be used to evaluate and examine the inner parts of the body. The idea is to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images, so as to provide as much details as possible for the sake of diagnosis. The objective of image fusion is to merge information from multiple images of the same image. The resultant image after image fusion is more suitable for human and machine perception and further helpful for image-processing tasks such as segmentation, feature extraction and object recognition. This paper mainly presents image fusion using wavelet method for multispectral data and high-resolution data conveniently, quickly and accurately in MATLAB. Wavelet toolbox with abundant functions, provide a quick and convenient platform to improve image visibility. The work covers the selection of wavelet function, the use of wavelet based fusion algorithms on CT and MRI medical images, implementation of fusion rules and the fusion image quality evaluation. Matlab Results show that effectiveness of Image Fusion with Wavelet Transform on preserving the feature information for the test images.

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Mr. Roshan P. Helonde

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Early Stage Brain Tumor Detection and Classification using Matlab Project Code

ABSTRACT

            A tumor is a mass of tissues that is formed by an accumulation of abnormal cells. Normally, the cells in our body grow, age, die, and are replaced by new cells but the cancer and other tumors damage this cycle. The tumor cells do grow, even if the body does not want them and unlike old cells, these cells do not die easily causing tumor or cancer. The brain is the interior most part of the central nervous system and is an intracranial solid neoplasm. Tumors are created by an abnormal and uncontrollable cell division in the brain. The axial view of the brain image scan has been used. The study of brain tumor is important as it is occurring in many people. In this project, an image segmentation method was proposed for the identification or detection of tumor from the brain. The methodology consists of the following steps: pre-processing by using grey-level, sharpening and median filters; segmentation of the image was performed by thresholding and also by applying the watershed segmentation. Finally the tumor region was obtained with its area and stage of cancer.

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Mr. Roshan P. Helonde

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Haze Removal using Image Processing Matlab Project with Source Code

ABSTRACT

           Haze causes problems in various computer vision and image processing based applications as it diminishes the scene's visibility. The air light and attenuation are two main phenomena responsible for haze formation .The air light enhance the whiteness in the scene and contrast get reduced by attenuation. Haze removal techniques helps in recovering the contrast and color of the scene. These techniques have found many applications in the area of image processing such as consumer electronics, object detection, outdoor surveillance etc. 

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Mr. Roshan P. Helonde

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Email: roshanphelonde@rediffmail.com

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Facial Expression Recognition using Matlab Project with Source Code

ABSTRACT

            This project objective is to introduce needs and applications of facial expression recognition. Between Verbal & Non-Verbal form of communication facial expression is form of non-verbal communication but it plays pivotal role. It express human perspective or filling & his or her mental situation. A big research has been addressed to enhance Human Computer Interaction (HCI) over two decades. This project includes introduction of facial emotion recognition system, Application, comparative study of popular face expression recognition techniques & phases of automatic facial expression recognition system. Emotional aspects have huge impact on Social intelligence like communication understanding, decision making and also helps in understanding behavioral aspect of human. Emotion play pivotal role during communication. Emotion recognition is carried out in diverse way, it may be verbal or non-verbal .Voice (Audible) is verbal form of communication & Facial expression, action, body postures and gesture is non-verbal form of communication. While communicating only 7% effect of message is contributes by verbal part as a whole, 38% by vocal part and 55% effect of the speaker’s message is contributed by facial expression. For that reason automated & real time facial expression would play important role in human and machine interaction. Facial expression recognition would be useful from human facilities to clinical practices. Analysis of facial expression plays fundamental roles for applications which are based on emotion recognition like Human Computer Interaction (HCI), Social Robot, Animation, Alert System & Pain monitoring for patients.

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Mr. Roshan P. Helonde

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Email: roshanphelonde@rediffmail.com

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Fish Disease Detection using Neural Network Matlab Project with Source Code

ABSTRACT

             There is a lot of expert knowledge in fish disease diagnostic process. Aimed at the drawbacks of expert diagnosis such as low level of intelligence and poor practicality, a new structure of fish disease diagnostic expert system based on neural networks is proposed. The diagnosis system uses the old cases of fish disease to train the neural network, and the disease diagnosis is achieved through the trained neural network. This method can realize the diagnosis of fish disease timely and rapidly, and provide a new effective method to the development of fish disease diagnosis system. 

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Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Bone Fracture Detection using Neural Network Matlab Project with source Code

ABSTRACT

            Analysis of medical images plays a very important role in clinical decision making. For a long time it has required extensive involvement of a human expert. However, recent progress in data mining techniques, especially in machine learning, allows for creating decision models and support systems that help to automatise this task and provide clinicians with patient-specific therapeutic and diagnostic suggestions. In this project, we describe a study aimed at building a decision model (a classifier) that would predict the type of treatment (surgical vs. non-surgical) for patients with bone fractures based on their X-ray images. We consider two types of features extracted from images (structural and textural) and used them to construct multiple classifiers that are later evaluated in a computational experiment. Structural features are computed by applying the Hough transform, while textural information is obtained from Gray-level occurrence matrix. In research reported by other authors structural and textural features were typically considered separately. Our findings show that while structural features have better predictive capabilities, they can benefit from combining them with textural ones.

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Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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