scispace - formally typeset
Search or ask a question
Journal ArticleDOI

An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding

TL;DR: A novel real time integrated method to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score.
Abstract: Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods.
Citations
More filters
Journal ArticleDOI
TL;DR: This study attempts to provide a comprehensive review of the fundamental processes required for change detection with a brief account of the main techniques of change detection and discusses the need for development of enhanced change detection methods.
Abstract: Change detection captures the spatial changes from multi temporal satellite images due to manmade or natural phenomenon. It is of great importance in remote sensing, monitoring environmental changes and land use –land cover change detection. Remote sensing satellites acquire satellite images at varying resolutions and use these for change detection. This paper briefly analyses various change detection methods and the challenges and issues faced as part of change detection. Over the years, a wide range of methods have been developed for analyzing remote sensing data and newer methods are still being developed. Timely and accurate change detection of Earth’s surface features provides the basis for evaluating the relationships and interactions between human and natural phenomena for the better management of resources. In general, change detection applies multi-temporal datasets to quantitatively analyse the temporal effects of the phenomenon. As such, this study attempts to provide a comprehensive review of the fundamental processes required for change detection. The study also gives a brief account of the main techniques of change detection and discusses the need for development of enhanced change detection methods.

196 citations


Cites background from "An Integrated Interactive Technique..."

  • ...(Hore et al. 2016) describes an imageTa bl e 5 D et ai le d su rv ey on se gm en ta tio n al go ri th m s A ut ho r D at a ba se Se gm en ta tio n A dv an ta ge s S ur es h an d L al (2 01 6) P lé ia de s S at el lit e Im ag er y C uc ko o se ar ch al go ri th m ba se d m ul til ev el th re sh ol…...

    [...]

Journal ArticleDOI
TL;DR: A hybrid semi-automated image processing methodology is proposed to inspect the ischemic stroke lesion using the MRI recorded with flair and diffusion-weighted modality to estimate the stroke severity and also to plan for further treatment process.
Abstract: Stroke is one of the widespread causes of morbidity worldwide and is also the foremost reason for attained disability in human community. Ischemic stroke can be confirmed by investigating the interior brain regions. Magnetic resonance image (MRI) is one of the noninvasive imaging techniques widely adopted in medical discipline to record brain malformations. In this paper, a hybrid semi-automated image processing methodology is proposed to inspect the ischemic stroke lesion using the MRI recorded with flair and diffusion-weighted modality. The proposed approach consists of two sections, namely the preprocessing based on the social group optimization monitored Fuzzy-Tsallis entropy and post-processing technique, which consists of a segmentation algorithm to extract the ISL from preprocessed image in order to estimate the stroke severity and also to plan for further treatment process. The proposed hybrid approach is experimentally investigated using the ischemic stroke lesion segmentation challenge database. This work also presents a detailed investigation among well-known segmentation approaches, like watershed algorithm, region growing technique, principal component analysis, Chan–Vese active contour, and level set approaches, existing in the literature. The results of the experimental work executed using ISLES 2015 challenge dataset confirm that proposed methodology offers superior average values for image similarity indices like Jaccard (78.60%), Dice (88.54%), false positive rate (3.69%), and false negative rate (11.78%). This work also helps to achieve improved value of sensitivity (99.65%), specificity (78.05%), accuracy (91.17%), precision (98.11%), BCR (90.19%), and BER (6.09%).

107 citations

Journal ArticleDOI
TL;DR: A two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities is proposed and it is confirmed that AC offers enhanced results compared with other segmentation procedures considered in this article.

61 citations


Cites background from "An Integrated Interactive Technique..."

  • ...In this work, commonly used ROI mining procedures, such as AC [30], MCWS [48] and SRG [37] approaches were implemented to extract the abnormal section from brain MRI of Flair/DW modality....

    [...]

  • ...SRG is one of the widely adopted semi-automated image segmentation scheme [8]....

    [...]

  • ...JI DC ACC PRE SEN SPE BCR 0 20 40 60 80 100 P er fo rm an ce m ea su re (% ) AC MCW S SRG Figure2....

    [...]

  • ...SRG is an operator initiated segmentation approach and widely adopted to mine the ROI in medical images....

    [...]

  • ...The JI offered by the MCWS was superior to AC and SRG, also the DC of SRG was better than the alternatives....

    [...]

Journal ArticleDOI
TL;DR: The experimental study established that the proposed two stage approach extracted efficiently the contrast enhanced regions from the MRA and T1C brain images.

57 citations

References
More filters
Journal ArticleDOI
TL;DR: This work uses snakes for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest, and uses scale-space continuation to enlarge the capture region surrounding a feature.
Abstract: A snake is an energy-minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges. Snakes are active contour models: they lock onto nearby edges, localizing them accurately. Scale-space continuation can be used to enlarge the capture region surrounding a feature. Snakes provide a unified account of a number of visual problems, including detection of edges, lines, and subjective contours; motion tracking; and stereo matching. We have used snakes successfully for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest.

18,095 citations


"An Integrated Interactive Technique..." refers methods in this paper

  • ...Image segmentation techniques can also be classified into five approaches, such as Thresholdingbased method [8], Boundary-based method [9], Region-based segmentation method [10], Active contour model (Snakes) image segmentation [11], hybrid segmentation technique [12]....

    [...]

Posted Content
TL;DR: E elegant connections between the concepts of Informedness, Markedness, Correlation and Significance as well as their intuitive relationships with Recall and Precision are demonstrated.
Abstract: Commonly used evaluation measures including Recall, Precision, F-Measure and Rand Accuracy are biased and should not be used without clear understanding of the biases, and corresponding identification of chance or base case levels of the statistic. Using these measures a system that performs worse in the objective sense of Informedness, can appear to perform better under any of these commonly used measures. We discuss several concepts and measures that reflect the probability that prediction is informed versus chance. Informedness and introduce Markedness as a dual measure for the probability that prediction is marked versus chance. Finally we demonstrate elegant connections between the concepts of Informedness, Markedness, Correlation and Significance as well as their intuitive relationships with Recall and Precision, and outline the extension from the dichotomous case to the general multi-class case.

5,092 citations

Journal ArticleDOI
TL;DR: This correspondence presents a new algorithm for segmentation of intensity images which is robust, rapid, and free of tuning parameters, and suggests two ways in which it can be employed, namely, by using manual seed selection or by automated procedures.
Abstract: We present here a new algorithm for segmentation of intensity images which is robust, rapid, and free of tuning parameters. The method, however, requires the input of a number of seeds, either individual pixels or regions, which will control the formation of regions into which the image will be segmented. In this correspondence, we present the algorithm, discuss briefly its properties, and suggest two ways in which it can be employed, namely, by using manual seed selection or by automated procedures. >

3,331 citations


"An Integrated Interactive Technique..." refers background in this paper

  • ...The foremost intention of region growing is to map individual pixels (seeds) in the input image to a set of pixels named region, which was first initiated by Adams and Bischof in 1994 [15]....

    [...]

Journal ArticleDOI
TL;DR: Examination of the mathematical relationship between Precision and Recall shows that a quadratic Recall curve can resemble empirical Recall–Precision behavior if transformed into a tangent parabola.
Abstract: Empirical studies of retrieval performance have shown a tendency for Precision to decline as Recall increases. This article examines the nature of the relationship between Precision and Recall. The relationships between Recall and the number of documents retrieved, between Precision and the number of documents retrieved, and between Precision and Recall are described in the context of different assumptions about retrieval performance. It is demonstrated that a tradeoff between Recall and Precision is unavoidable whenever retrieval performance is consistently better than retrieval at random. More generally, for the Precision–Recall trade-off to be avoided as the total number of documents retrieved increases, retrieval performance must be equal to or better than overall retrieval performance up to that point. Examination of the mathematical relationship between Precision and Recall shows that a quadratic Recall curve can resemble empirical Recall–Precision behavior if transformed into a tangent parabola. With very large databases and/or systems with limited retrieval capabilities there can be advantages to retrieval in two stages: Initial retrieval emphasizing high Recall, followed by more detailed searching of the initially retrieved set, can be used to improve both Recall and Precision simultaneously. Even so, a tradeoff between Precision and Recall remains. © 1994 John Wiley & Sons, Inc.

714 citations

Journal ArticleDOI
TL;DR: A bottom-up aggregation approach to image segmentation that takes into account intensity and texture distributions in a local area around each region and incorporates priors based on the geometry of the regions, providing a complete hierarchical segmentation of the image.
Abstract: We present a bottom-up aggregation approach to image segmentation. Beginning with an image, we execute a sequence of steps in which pixels are gradually merged to produce larger and larger regions. In each step, we consider pairs of adjacent regions and provide a probability measure to assess whether or not they should be included in the same segment. Our probabilistic formulation takes into account intensity and texture distributions in a local area around each region. It further incorporates priors based on the geometry of the regions. Finally, posteriors based on intensity and texture cues are combined using “ a mixture of experts” formulation. This probabilistic approach is integrated into a graph coarsening scheme, providing a complete hierarchical segmentation of the image. The algorithm complexity is linear in the number of the image pixels and it requires almost no user-tuned parameters. In addition, we provide a novel evaluation scheme for image segmentation algorithms, attempting to avoid human semantic considerations that are out of scope for segmentation algorithms. Using this novel evaluation scheme, we test our method and provide a comparison to several existing segmentation algorithms.

511 citations


"An Integrated Interactive Technique..." refers methods in this paper

  • ...The Weizmann institute of science database [17] is used for the experimental results....

    [...]