Background: Digital Signal Processing (D.S.P) is an evolutionary field. It has a vast variety of applications in all fields. Bio medical engineering has various applications of digital signal processing. Digital Image Processing is one of the branches of signal processing. Medical image modalities proved to be helpful for disease diagnosis. Higher expertise is required in image analysis by medical professional, either doctors or radiologists. Methods: Extensive research is being done and has produced remarkable results. The study is divided into three main parts. The first deals with introduction of mostly used imaging modalities such as, magnetic resonance imaging, x-rays, ultrasound, positron emission tomography and computed tomography. The next section includes explanation of the basic steps of digital image processing are also explained in the paper. Magnetic Resonance imaging modalities is selected for this research paper. Different methods are tested on MRI images. Discussion: Brain images are selected with and without tumor. Solid cum Cystic tumor is opted for the r esearch. Results are discussed and shown. The software used for digital image processing is MATLAB. It has in built functions which are used throughout the study. The study represents the importance of DIP for tumor segmentation and detection. Conclusion: This study provides an initial guideline for researchers from both fields, that is, medicine and engineering. The analyses are shown and discussed in detail through images. This paper shows the significance of image processing platform for tumor detection automation.
Abstract-The primary purpose of this paper is to elaborate upon and to take a step ahead on the research done in the field of Image Processing with a focus on Early Tumor Detection through the use of Magnetic Resonance Imaging (MRI) and Image Processing Tool Box of MATLAB. The technique which the author proposed is Morphological Reconstruction Based Segmentation, used to segment the solid cum cystic tumor of brain and is suggested after testing and observing various available methods and algorithms. The proposed method shows more precision amongst others and the processing time is also fast.
Segmentation of brain tumors has been found challenging throughout in the field of image processing. Different algorithms have been applied to the segmentation of solid or cystic tumors individually but little work has been done for solid cum cystic tumor. The papers reviewed in this article only deal with the case study of patients suffering from solid cum cystic brain tumor as this type of tumor is rarely found for the purpose of research. The research work conducted so far on this topic has been reviewed. The study begins with 2D (Two Dimensional) segmentation of tumor using MATLAB. It is then extended to study of slices of tumor and its volume calculation using open source software named 3D Slicer which represents the tumor in 3D. This software can intake the 2D slices and process them to give a combined 3D view. Various techniques are available in the software. According to the particular requirement an appropriate algorithm can be chosen. This paper gives a promising hierarchy for volume calculation of tumor and the three dimensional view. Further we can also find the position of tumor in the skull using the same software. This piece of work is a valuable guideline for the researchers interested in segmentation and three dimensional representations of different areas of human body. The models extracted out using the given algorithms can also be treated for matching and comparison of any future research. This will also aid surgeons and physicians in efficient analysis and reporting techniques.
This piece of work investigates the application of histogram equalization method to clinical images for noise removal and efficient image enhancement without any information loss. Computed tomographic (CT) images of the abdomen bearing liver tumour are kept under study. Liver exhibits heterogeneous combination of intensities which makes it a challenging task to enhance the liver tumour embedded in the image. Distortion occurs due to the presence of quantum noise in the CT scans and important information of the image is suppressed. The methodology adopted in this paper comprises of two stages. Initially pixel based intensity transformation is adopted for de-noising the background of the image by the selection of appropriate threshold levels. The resultant image gives a noise free background and the foreground features are enhanced. In the next stage histogram equalization filters are applied to the transformed image. The equalization method which gives uniform image enhancement with lesser mean square error (MSE) and increased peak signal to noise ratio (PSNR) is supposed to be an effective method for efficient enhancement of the images. This study deals with the application of histogram equalization methods to CT images which can aid the radiologists for better visualization and diagnosis of the disease.
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