2,901 research outputs found
A comparative evaluation of interactive segmentation algorithms
In this paper we present a comparative evaluation of four popular interactive segmentation algorithms. The evaluation was carried out as a series of user-experiments, in which participants were tasked with extracting 100 objects from a common dataset: 25 with each algorithm, constrained within a time limit of 2 min for each object. To facilitate the experiments, a âscribble-drivenâ segmentation tool was developed to enable interactive image segmentation by simply marking areas of foreground and background with the mouse. As the participants refined and improved their respective segmentations, the corresponding updated segmentation mask was stored along with the elapsed time. We then collected and evaluated each recorded mask against a manually segmented ground truth, thus allowing us to gauge segmentation accuracy over time. Two benchmarks were used for the evaluation: the well-known Jaccard index for measuring object accuracy, and a new fuzzy metric, proposed in this paper, designed for measuring boundary accuracy. Analysis of the experimental results demonstrates the effectiveness of the suggested measures and provides valuable insights into the performance and characteristics of the evaluated algorithms
Research Pattern Classification using imaging techniques for Infarct and Hemorrhage Identification in the Human Brain
Medical Image analysis and processing has great
significance in the field of medicine, especially in Non-
invasive treatment and clinical study. Medical imaging
techniques and analysis tools enable the Doctors and
Radiologists to arrive at a specific diagnosis. Medical Image
Processing has emerged as one of the most important tools
to identify as well as diagnose various disorders. Imaging
helps the Doctors to visualize and analyze the image for
understanding of abnormalities in internal structures. The
medical images data obtained from Bio-medical Devices
which use imaging techniques like Computed Tomography
(CT), Magnetic Resonance Imaging (MRI) and
Mammogram, which indicates the presence or absence of
the lesion along with the patient history, is an important
factor in the diagnosis. The algorithm proposes the use of
Digital Image processing tools for the identification of
Hemorrhage and Infarct in the human brain, by using a
semi-automatic seeded region growing algorithm for the
processing of the clinical images. The algorithm has been
extended to the Real-Time Data of CT brain images and
uses an intensity-based growing technique to identify the
infarct and hemorrhage affected area, of the brain. The
objective of this paper is to propose a seeded region
growing algorithm to assist the Radiologists in identifying
the Hemorrhage and Infarct in the human brain and to arrive
at a decision faster and accurate.¢Lp¤
Automatic Determination of Seeds for Random Walker by Seeded Watershed Transform for Tuna Image Segmentation
Tuna fish image classification is an important part to sort out the type and quality of the tuna based upon the shape. The image of tuna should have good segmentation results before entering the classification stage. It has uneven lighting and complex texture resulting in inappropriate segmentation. This research proposed method of automatic determination seeded random walker in the watershed region for tuna image segmentation. Random walker is a noise-resistant segmentation method that requires two types of seeds defined by the user, the seed pixels for background and seed pixels for the object. We evaluated the proposed method on 30 images of tuna using relative foreground area error (RAE), misclassification error (ME), and modified Hausdroff distances (MHD) evaluation methods with values of 4.38%, 1.34% and 1.11%, respectively. This suggests that the seeded random walker method is more effective than exiting methods for tuna image segmentation
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