Improvised Method Of Tumor Detection Using Saliency

In the video “The future of early cancer detection?” by Jorge Soto, he explained how detecting early stages of cancer is vital because it’ll save lives of many patients and loss of love ones. For example, Soto described how his aunt was suffering from back pains and how the doctor thought it was due to playing tennis and discarded the thoughts that it might be something more. As the aunt kept coming back to the doctor, they did a biopsy. But later on, they found out that Soto’s aunt was in stage three of cancer. After his story, Soto demonstrated a low-cost machine that can detect early signs of cancer. I believed with Soto that doctor won’t treat their patients until their patients start showing symptoms and by that time, their patients’ health

When the doctor says to the patient that he has a tumor the first question comes to his mind if it is malignant or benign, but there is an important information that people do not know. Malignant and benign tumors have similar characteristics. First of all, a benign tumor can become a huge mass. Similarly, a malignant tumor has different sizes. For example, since scientists discovered the disease until these days they have been removed billions of masses weighing over a hundred pounds of malignant and benign tumors. In 2012, a Vietnamese man underwent a 12-hour operation to remove a 200-pound tumor from his leg. Nevertheless, it depends on how fast the cells are separating. Like the malignant tumor, benign tumor can be treated. For instance,

After reading the digital image into internal memory, the program uses cvThreshold function to transfer the colorful image into a grayscale one. Then according to the optimal variance, transfer the grayscale image to a binary image. After that, the program uses cvDilate and cvErode functions to do dilation and erosion operation. Thus, in order to improve the accuracy rate, cvSmooth function is used to smooth the edges of the image. Then program uses canny operator to detect and outline edges. At the end, circle hough transform is used to detect the circles in the images, which are the sign of human head. So the program

Manual segmentation of this CT scans are tedious and prohibitively time-consuming for a clinical setting. Automatic segmentation on the other hand, is a very challenging task, due to various factors, such as liver stretch over 150 slices in a CT image, indefinite shape of the lesions and low intensity contrast between lesions and similar to those of nearby tissues. The irregularity in the liver shape and size between the patients and the similarity with other organs of almost same intensity make automatic liver segmentation

We have presented a new framework to analyze the brain image and identify the disorder associated with it. We have mainly focused on identification of Tumor and classification of Age and Gender. Tumor identification is an important thing in medical imaging because it is helpful to update the condition of human being. Tumor segmentation is used to calculate the location and size of tumor using Modified RBF algorithm. If it is malignant tumor, then patients need to treat immediately. So, severity of the disorder can be found using tumor segmentation and accordingly medication can be given. Many previous work have done to find the disorder but it is the first time, we tried to identify the age and gender. Age is classified as Child, Old and Adult

The particular finding parameters (i.e. threshold, fwhm, sharpness, and roundness) were tuned to the data to maximize true identification yields as determined by visual inspection. Some parameters like fwhm and signal threshold were set adaptively by using image based diagnostics like a first order estimate of the Gaussian fit PSF and the background noise levels, respectively. It was particularly tricky to find parameters that worked well for both clear and highly nebulous regions with small features. Also, the parameters had to be tuned for particular filters and co-adding depths. The subsequent photometry routines for grouping, nstar fitting, and PSF subtraction were all part of the Photoutils package. A sigma clipped median was used to estimate the

Medical image processing is the demanding task and emerging field. Medical imaging procedure will view the images that are present in internal portions of the human body for medical analysis. Automatic Brain tumor segmentation is a perceptive stage in medical field. In the MR images, the tumor part can be seen clearly by the accurate size and the correct measurement for the treatment. A necrotic part can be differentiated from the surrounding tissue. The automatic image segmentation algorithm, which exploit the information obtain from deducting tumor in brain MRI. Segmentation algorithm that will segment brain MR images into tumor, white matter, gray matter and cerebrospinal fluid separately. In this paper, improved automatic image segmentation

Division is the technique, separating a photo into areas with comparative properties such as dark level, shading, surface, shine, and difference [22]. The inspiration driving division is to subdivide the items in an image; Just if there should arise an occurrence of therapeutic picture division the point is to: Study anatomical structure, build up an Area of Interest i.e. discovers neoplasm (tumor), injury and distinctive variations from the norm, live tissue volume to live development of neoplasm (likewise diminish in size of neoplasm with treatment) and encourage in treatment arranging before radiation therapy; in radiation dosage estimation. Programmed division of medicinal pictures is a hard assignment as therapeutic pictures are mind boggling

Padole et al. [PAD12] proposed an efficient technique for brain tumor detection. One of the maximum essential steps in tumor detection is segmentation. Combination of general algorithms, suggest shift and normalized cut is executed to hit upon the brain tumor surface area in MRI. Pre-processing step is first done by way of the use of the imply shift set of rules as a way to shape segmented regions. Inside the next step location nodes clustering are processed by way of n-cut approach. Inside the final step, the mind tumor is detected through element

4 different thresholding algorithms are calculated and the one which covers results of other selected thresholds is applied on the optimal single-channel image

Motta et al. [6] developed an automated segmentation method, which used thresholding based on highest temperature of the breast for the detection of lower border. Upper limit of region of interest (ROI) is obtained by detecting axilla. The center point of the segmented image is used for separating right and left breast. But in an asymmetric breast, the center point of the breast may not detect right and left breast ROI separation point.

Tumor segmentation from magnetic resonance imaging (MRI) data is an important but time consuming manual task performed by medical experts. Automating this process is a challenging task because of the high diversity in the appearance of tumor tissues among different patients and in many cases similarity with the normal tissues. MRI is an advanced medical imaging technique providing rich information about the human soft-tissue anatomy.. In this paper, we have proposed an automatic tumor detection framework to detect the multiple tumors in brain tumor databases. This system has four main phases, namely image preprocessing for image enhancement, Fuzzy C-Means segmentation algorithm is used for tumor segmentation, Apply thresholding on segmented

Neurodegenerative diseases causes a wide variety of mental symptoms whose evolution is not directly related to the analysis made by radiologists on basis of images, who can hardly quantify systematic differences. This paper presents a new automatic (Based on software program) image analysis method that reveals different brain patterns associated to the presence of neurodegenerative diseases, finding systematic differences and therefore grading objectively any neurological disorder. An accurate solution can be provided by using Alzheimer’s diseases based on saliency map characterization is carried out on database images. This paper gives automatic image analysis method and attempts an approach for classification of brain images to search for pathology and normality part of brain by extracting salient features of input brain image and the region of interest is identified using kernel k-means algorithm. A support vector machine (SVM) a supervised learning process is used for classification of AD, which is recognized on basis of blue color is normal brain part and red color is pathology related.

Paper [1] presented Integrated Multiple Features for Tumor Image Retrieval Using Classifier and Feedback Methods. This paper presents an effective approach in which the region of the object is extracted with the help of multiple features ignoring the background of the object by employing edge following segmentation method followed by extracting texture and shape characteristics of the images. The former is extracted with the help of Steerable filter at different orientations and radial Chebyshev moments are used for extracting the later.

“The aim of screening a population for cancer is to make the diagnosis early and thereby increase the cure rate.” (Tobias and Hochhauser,. Cancer and its Management, 2010 p21)