Watershed based intelligent scissors

Więcławek, Wojciech; Piętka, Ewa

doi:10.1016/j.compmedimag.2015.01.003

Cited by 14 publications

(7 citation statements)

References 22 publications

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“…The Watershed algorithm does have some disadvantages, such as over segmenting on the liver muscle area and density measures [25] [26]. It has been reported that there is some improvement that do need to be found while used to analyze liver images, such as corridor reduction [27].…”

Section: Introductionmentioning

confidence: 99%

Morphological Segmentation Analysis and Texture-based Support Vector Machines Classification on Mice Liver Fibrosis Microscopic Images

Wang

Shi

Cao

et al. 2019

CBIO

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Background: To reduce the intensity of the work of doctors, pre-classification work needs to be issued. In this paper, a novel and related liver microscopic image classification analysis method is proposed. Objective: For quantitative analysis, segmentation is carried out to extract the quantitative information of special organisms in the image for further diagnosis, lesion localization, learning and treating anatomical abnormalities and computer-guided surgery. </P><P> Methods: In the current work, entropy-based features of microscopic fibrosis mice’ liver images were analyzed using fuzzy c-cluster, k-means and watershed algorithms based on distance transformations and gradient. A morphological segmentation based on a local threshold was deployed to determine the fibrosis areas of images. Results: The segmented target region using the proposed method achieved high effective microscopy fibrosis images segmenting of mice liver in terms of the running time, dice ratio and precision. The image classification experiments were conducted using Gray Level Co-occurrence Matrix (GLCM). The best classification model derived from the established characteristics was GLCM which performed the highest accuracy of classification using a developed Support Vector Machine (SVM). The training model using 11 features was found to be accurate when only trained by 8 GLCMs. Conclusion: The research illustrated that the proposed method is a new feasible research approach for microscopy mice liver image segmentation and classification using intelligent image analysis techniques. It is also reported that the average computational time of the proposed approach was only 2.335 seconds, which outperformed other segmentation algorithms with 0.8125 dice ratio and 0.5253 precision.</P>

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Section: Introductionmentioning

confidence: 99%

Morphological Segmentation Analysis and Texture-based Support Vector Machines Classification on Mice Liver Fibrosis Microscopic Images

Wang

Shi

Cao

et al. 2019

CBIO

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“…Many techniques can be employed into segmentation. Those techniques could not be a portion of image processing, such as mathematical morphology and watershed 10,11 hard or fuzzy clustering, 12–14 fuzzy connectedness, 15–17 level sets, 18,19 deformable models, 20 or intelligent scissors 11,21 . Many main ideas need to be considered during segmentation including edge‐based segmentation, different layers of automated segmentation and multidimensional images operation 22–24 .…”

Section: Introductionmentioning

confidence: 99%

“…Those techniques could not be a portion of image processing, such as mathematical morphology and watershed 10,11 hard or fuzzy clustering, 12-14 fuzzy connectedness, 15-17 level sets, 18,19 deformable models, 20 or intelligent scissors. 11,21 Many main ideas need to be considered during segmentation including edge-based segmentation, different layers of automated segmentation and multidimensional images operation. [22][23][24] This is resulting in the complexity of defining a universal segmentation technique because segmentation algorithms are problem-based in terms of image source and dimensionality, type of organ, and its representation within the image.…”

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confidence: 99%

Transferable HMM probability matrices in multi‐orientation geometric medical volumes segmentation

AlZu’bi

Al-Qatawneh

Elbes

et al. 2019

Concurrency and Computation

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Acceptable error rate, low quality assessment, and time complexity are the major problems in image segmentation, which needed to be discovered. A variety of acceleration techniques have been applied and achieve real time results, but still limited in 3D. HMM is one of the best statistical techniques that played a significant rule recently. The problem associated with HMM is time complexity, which has been resolved using different accelerator. In this research, we propose a methodology for transferring HMM matrices from image to another skipping the training time for the rest of the 3D volume. One HMM train is generated and generalized to the whole volume. The concepts behind multi-orientation geometrical segmentation has been employed here to improve the quality of HMM segmentation. Axial, saggital, and coronal orientations have been considered individually and together to achieve accurate segmentation results in less processing time and superior quality in the detection accuracy. KEYWORDS hidden Markov model, image segmentation, medical imaging, transfer learning, 3D volumes 1 INTRODUCTION Nowadays, segmentation is one of the key problems in the field of computer vision. The understanding of a scene is considered to be the main problem of different computer-based applications that nourish from inferring knowledge from imagery, such as self-driving vehicles, human-computer interaction, virtual reality, medical applications, and many others. The growing interest of deep learning has added an enormous boost to the already rapidly developing field of computer vision where many semantic segmentation problems are being tackled using deep architectures. In the early stages of this century, civilization became more picture based. The employment of image processing and analysis is widely broadcast in many activities more than ever been. The expansion of social networks and due to globalization mechanisms helps in employing image processing analysis in this era. 1 Computer-aided diagnosis (CAD) systems are computer software, which have been mainly implemented for helping specialists doing their jobs; many applications have employed computer systems for better quality such as those in other works. 2-5 These software techniques improve diagnosing medical data. Big data and high-speed processing of this data helps the physician reach an impressive and high-quality diagnosis in real time with the mentioned software techniques or systems from possibly different sources. 6 Image segmentation plays an important role in both natural and medical analysis. 7 It is the process of segmenting or partitioning a digital image into a set of separated regions according to some connected parts. 8,9 Volume segmentation can be defined as declaring the voxels, which comprise the contour edges and the volume of objects of interest. Many techniques can be employed into segmentation. Those techniques could not be a portion of image processing, such as mathematical morphology and watershed 10,11 hard or fuzzy clustering, 12-14 fuzzy connectedness...

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“…Sometimes, even for a human, it can be hard to extract nuclei from clumps. Nuclei had until recently been segmented using classical segmentation methods such as intensity thresholding, the watershed method or active contours (Irshad et al, 2014;Yang et al, 2006;Więcławek and Piętka, 2015;Koyuncu et al, 2016;Piórkowski, 2016;Paramanandam et al, 2016;Kłeczek et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

The Feature Selection Problem in Computer–Assisted Cytology

Kowal

Skobel

Nowicki

2018

International Journal of Applied Mathematics and Computer Science

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Modern cancer diagnostics is based heavily on cytological examinations. Unfortunately, visual inspection of cytological preparations under the microscope is a tedious and time-consuming process. Moreover, intra-and inter-observer variations in cytological diagnosis are substantial. Cytological diagnostics can be facilitated and objectified by using automatic image analysis and machine learning methods. Computerized systems usually preprocess cytological images, segment and detect nuclei, extract and select features, and finally classify the sample. In spite of the fact that a lot of different computerized methods and systems have already been proposed for cytology, they are still not routinely used because there is a need for improvement in their accuracy. This contribution focuses on computerized breast cancer classification. The task at hand is to classify cellular samples coming from fine-needle biopsy as either benign or malignant. For this purpose, we compare 5 methods of nuclei segmentation and detection, 4 methods of feature selection and 4 methods of classification. Nuclei detection and segmentation methods are compared with respect to recall and the F1 score based on the Jaccard index. Feature selection and classification methods are compared with respect to classification accuracy. Nevertheless, the main contribution of our study is to determine which features of nuclei indicate reliably the type of cancer. We also check whether the quality of nuclei segmentation/detection significantly affects the accuracy of cancer classification. It is verified using the test set that the average accuracy of cancer classification is around 76%. Spearman's correlation and chi-square test allow us to determine significantly better features than the feature forward selection method.

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Watershed based intelligent scissors

Cited by 14 publications

References 22 publications

Morphological Segmentation Analysis and Texture-based Support Vector Machines Classification on Mice Liver Fibrosis Microscopic Images

Morphological Segmentation Analysis and Texture-based Support Vector Machines Classification on Mice Liver Fibrosis Microscopic Images

Transferable HMM probability matrices in multi‐orientation geometric medical volumes segmentation

The Feature Selection Problem in Computer–Assisted Cytology

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