Set Of Image Processing Operations Research Articles

Automated Cell Nuclei Segmentation on Cervical Smear Images Using Structure Analysis

Cervical cancer is a common cancer that affects women around the world, and it is also the most common cancer in the developing countries. The cancer burden has increased due to several factors, such as population growth and ageing. In the early century, the systematization of cervical cancer cells takes some time to process manually, and the result that comes out is also inaccurate. This article presents a new nucleus segmentation on pap smear cell images based on structured analysis or morphological approach. Morphology is a broad set of image processing operations that process images based on shape, size and structure. This operation applies a structural element of the image to create an output image of the same size. The most basic of these operations are dilation and erosion. The results of the numerical analysis indicate that the proposed method achieved about 94.38% (sensitivity), 82.56% (specificity) and 93% (accuracy). Also, the resulting performance was compared to a few existing techniques such as Bradley Method, Nick Method and Sauvola Method. The results presented here may facilitate improvements in the detection method of the pap smear cell image to resolve the time-consuming issue and support better system performance to prevent low precision result of the Human Papilloma Virus (HPV) stages. The main impact of this paper is will help the doctor to identify the patient disease based on Pap smear analysis such as cervical cancer and increase the percentages of accuracy compared to the conventional method. Successful implementation of the nucleus detection techniques on Pap smear image can become a standard technique for the diagnosis of various microbiological infections such as Malaria and Tuberculosis.

Fully automatic brain tumor extraction and tissue segmentation from multimodal MRI brain images

AbstractSegmentation of Brain tumor from the magnetic resonance imaging (MRI) of head scans is an essential requirement for clinical diagnosis since manual segmentation is a fatigue and time‐consuming process. Recent computer‐aided diagnosis systems depend on the development of fully automatic methods to overcome these problems. In the present work, a fully automated algorithm is proposed to extract and segment tumor regions from multimodal magnetic resonance imaging (MMMRI) sequences. The algorithm has three phases: (a) tumor portion extraction, (b) tumor substructure segmentation, and (c) 3D postprocessing. First, the algorithm extracts tumor portion using a set of image processing operations from T2, fluid‐attenuated inversion recovery (FLAIR), and T1C images. Here, the proposed modified fuzzy c means clustering algorithm is used for enhancing the tumor portion extraction process. Then, the substructures of tumor such as edema, enhancing tumor, and necrotic regions are segmented from MMMRI sequences, T2, FLAIR, and T1C using region‐wise set operations in Phase II. Finally, 3D visualization of the segmented tumor and volume estimation is performed as postprocessing in Phase III. The proposed work was experimented on BraTS 2013 dataset. The quantitative analysis is performed using William's Index, Dice, sensitivity, specificity, and accuracy and is compared with 19 state‐of‐the‐art methods. The proposed method yields comparable results as 77%, 53%, and 59% of Dice for complete, core, and enhancing tumor regions, respectively.

An effective hybrid technique for image enhancement through Genetic Algorithm-morphological operations

Recently, digital images play a vital role in the society because of its extensive applications and a wide range of researches are carried out in the field of image processing. Digital Image Processing plays a vital role in numerous fields such as medical, remote sensing etc. Image enhancement is an essential process, because elucidation is a prerequisite for facilitating the application of any other process on images. The role played by image enhancement is considered to be more significant than that of other processes because the other processes are generally carried out, after acquiring the clarified image. Here, the image is enhanced using a hybrid method that combines the Genetic Algorithm (GA) and the morphological operation. The intensity and the contrast of the image are attuned using GA. First, the image is converted to gray scale image and then using GA the contrast parameter of the gray scale image are adjusted based on histogram equalisation and then morphological operation. Morphology used to process images based on shapes is a large set of image processing operations. Thus an enhanced image is suitable for further image processing applications is obtained. The obtained enhanced image also has perfect clarity for human perception.

A system for segmenting and extracting paper-based watermark designs

This paper presents a framework for the digitisation, extraction, and graphical representation of paper-based watermark designs embedded in paper texture: there is a growing need for this among librarians and antiquarians to aid with classification and preservation. The system is designed to handle manuscripts with foreground interference and defects; it uses a back-lighting scanning technique combined with image processing operations rather than radioactive techniques. Hence, it is faster, cheaper, safer, and easy to use. The system prototype includes a set of image processing operations which enhance, filter, and extract the watermark shape, and automatically convert it into a graphical representation. The paper focuses also on automated processes which determine the configuration of parameters in order to allow optimal content processing, in addition to the detection of watermark chainlines. With a machine readable graphical representation of the watermark, cataloguing and indexing of these heritage resources can be enhanced with the ease of digital content retrieval functionalities exploiting the advantages of digital technologies such as distribution and preservation.

Computational Models of MRI Characteristics of Focal Cortical Dysplasia Improve Lesion Detection

In many patients, focal cortical dysplasia (FCD) is characterized by minor structural changes that may go unrecognized by standard radiological analysis. We previously demonstrated that visual analysis of a composite map based on three simple models of MRI features of FCD increased the sensitivity of FCD lesion detection, compared to visual analysis of conventional MRI. Here we report on the use of improved methods for characterizing FCD which improve contrast in the composite maps: a Laplacian-based metric for measuring cortical thickness, a convolutional kernel to model blurring of the GM–WM interface, and an operator to measure hyperintense T1 signal. To validate these methods, we processed the MRIs of 14 FCD patients with our original set of image processing operators and an improved set of image processing operators. Comparison of the composite maps associated with the two sets of operators revealed that contrast between lesional tissue and nonlesional cortex was significantly increased in the composite maps associated with the set of improved operators. Increasing this contrast is an important step toward the goal of automated FCD lesion detection.

An Analytical Method for Predicting the Performance of Parallel Image Processing Operations

This paper presents an analytical performance prediction model and methodology that can be used to predict the execution time, speedup, scalability and similar performance metrics of a large set of image processing operations running on a p-processor parallel system. The model which requires only a few parameters obtainable on a minimal system can help in the systematic design, evaluation and performance tuning of parallel image processing systems. Using the model one can reason about the performance of a parallel image processing system prior to implementation. The method can also support programmers in detecting critical parts of an implementation and system designers in predicting hardware performance and the effect of hardware parameter changes on performance. The execution of parallel image processing operations was studied and operations were arranged in three main problem classes based on data locality and the communication patterns of the algorithms. The core of the method is the derivation of the overhead function, as it is the overhead that determines the achievable speedup. The overheads were examined and modelled for each class. The use of the method is illustrated by four class-representative image processing algorithms: image-scalar addition, convolution, histogram calculation and the Fast Fourier Transform. The developed performance model has been validated on a 16-node parallel machine and it has been shown that the model is able to predict the parallel run-time and other performance metrics of parallel image processing operations accurately.

Computer vision on magnetic resonance images

We present an approach for the automated interpretation of transaxial cranial magnetic resonance images. After a brief outline of our notation and basic assumptions, the overall design consisting of a neurological inference engine, a set of image processing operators and a configurating component for these operators is presented.