A Survey on Image Mining Techniques: Theory and Applications

Image mining is a vital technique which is used to mine knowledge straightforwardly from image. Image segmentation is the primary phase in image mining. Image mining is simply an expansion of data mining in the field of image processing. Image mining handles with the hidden knowledge extraction, image data association and additional patterns which are not clearly accumulated in the images. It is an interdisciplinary field that integrates techniques like computer vision, image processing, data mining, machine learning, data base and artificial intelligence. The most important function of the mining is to generate all significant patterns without prior information of the patterns. Rule mining has been adopting to huge image data bases. Mining has been done in accordance with the integrated collections of images and its related data. Numerous researches have been carried on this image mining. This paper presents a survey on various image mining techniques that were proposed earlier in literature. Also, this paper provides a marginal overview for future research and improvements. Keywords— Data Mining, Image Mining, Knowledge Discovery, Segmentation, Machine Learning, Artificial Intelligence, Rule Mining, Datasets

Content Based Image Retrieval based on Shape with Texture Features

In areas of state, domain and hospitals, massive collections of digital pictures are being created. These image collections are the merchandise of digitizing existing collections of analogue images, diagrams, drawings, paintings, and prints. Retrieving the specified similar image from a large dataset is very difficult. A new image retrieval system is obtainable in this paper, for feature extraction HSV color space and wavelet transform approach are used. Initially constructed one dimension feature vector and represented the color feature it is made by that the color space is quantified in non-equal intervals. Then with the help of wavelet texture feature extraction is obtained. At last by using of wavelet transform combined the color feature and texture feature method. The illustration features are susceptible for different type images in image retrieval experiments. The color features opted to the rich color image with simple variety. Texture feature opted to the complex images. At the same time, experiments reveal that HSV texture feature based on wavelet transform has better effective performance and stability than the RGB. The same work is performed for the RGB color space and their results are compared with the proposed system. The result shows that CBIR with the HSV color space is retrieves image with more accuracy and reduced retrieval time. Keywords--Content Based Image Retrieval, HSV, RG

An Efficient CBIR Technique with YUV Color Space and Texture Features

In areas of government, academia and hospitals, large collections of digital images are being created. These image collections are the product of digitizing existing collections of analogue photographs, diagrams, drawings, paintings, and prints. Retrieving the specified similar image from a large dataset is very difficult. A new image retrieval system is presented in this paper, which used YUV color space and wavelet transform approach for feature extraction. Firstly, the color space is quantified in non-equal intervals, then constructed one dimension feature vector and represented the color feature. Similarly, the texture feature extraction is obtained by using wavelet. Finally, color feature and texture feature are combined based on wavelet transform. The image retrieval experiments specified that visual features were sensitive for different type images. The color features opted to the rich color image with simple variety. Texture feature opted to the complex images. At the same time, experiments reveal that YUV texture feature based on wavelet transform has better effective performance and stability than the RGB and HSV. The same work is performed for the RGB and HSV color space and their results are compared with the proposed system. The result shows that CBIR with the YUV color space retrieves image with more accuracy and reduced retrieval time. Keywords---Content based image retrieval, Wavelet transforms, YUV, HSV, RG

Experimental Investigation for Detecting Mitotic Cells in Medical Image using an Automated Algorithm

Cancer of the breast is a malignant tumour that originates in the cells of the breast tissue. It is by far the most common kind of cancer found in females around the world, with a projected 2.3 million new cases will be discovered in the year 2020 alone. It is projected that one in eight women will be diagnosed with breast cancer at some point in their life, despite the fact that breast cancer can also occur in men. Breast cancer is a complex condition that can arise from a diverse set of factors, express itself in a variety of ways, and can be treated in a variety of ways. Ductal carcinoma in situ, invasive ductal carcinoma, and invasive lobular carcinoma are all different subtypes. Both the available treatment options and the expected outcome of breast cancer are very variable depending on the particular subtype of the illness. Breast cancer risk factors include drinking alcohol and not getting enough exercise, as well as getting older, having a family history of the disease, having genetic mutations, being exposed to estrogens, and having a family history of the disease. There is not always a connection between having risk factors and developing breast cancer, despite the fact that there can be a link between the two. The prognosis and treatment options for breast cancer are highly dependent on the stage of the disease at the time of diagnosis. During staging, the extent to which the cancer has spread throughout the body and how far it has progressed are both measured. The TNM system, the IAFCM system, the ACM system, and the MPIG system are just few of the staging systems that are used to classify breast cancer. These staging systems consider not only the size of the tumor but also whether or not lymph nodes are involved and whether or not distant metastases are present. The severity of breast cancer symptoms can vary widely, depending not only on the subtype of the disease but also on how far along it has progressed. Alterations in the size or shape of the breast, discharge from the nipple, and alterations in the skin of the breast (such as redness or dimpling) are all common indications. On the other hand, not all cases of breast cancer present themselves in a visible manner, and mammography and other forms of routine screening may be able to detect some of these cases. Options for treating breast cancer vary depending on the patient's condition and the stage of the disease, as well as the patient's overall health and their preferences towards therapy. Common examples of medical interventions include surgery, radiotherapy, chemotherapy, hormone therapy, and targeted therapy. Other examples include. In certain cases, it may be appropriate to participate in more than one form of treatment