A mathematical morphological approach for segmenting heavily noise-corrupted images

Lee, C.K.; Wong, S.P.

doi:10.1016/0031-3203(96)86888-9

Cited by 22 publications

(7 citation statements)

References 12 publications

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“…Lee, C.K., and Wong, S.P. [18] proposed an image segmentation method to remove noise from images based on mathematical grey-scale morphology. We have integrated some of the features of their method, such as erosion and dilation, with our proposed method.…”

Section: A Background Removalmentioning

confidence: 99%

“…To compute the cyclomatic complexity, we have used a python tool called "Radon". The complexity is ranked from A to F based on the score, where 'A' (1-5) denotes the most simple and best code, 'B' (6-10) denotes well-structured and stable blocks, 'C' (11)(12)(13)(14)(15)(16)(17)(18)(19)(20) denotes moderate and slightly complex block and 'F' (41+) denotes very high risk and unstable code. Cyclomatic complexity is calculated by using the equation M = E -N + 2P, where E is the number of edges in the control flow graph of the program, N is the number of nodes in the graph and P is the number of connected components.…”

Section: B Cyclomatic Complexitymentioning

confidence: 99%

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Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN

et al. 2021

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Breast cancer is the most diagnosed cancer in Australia with crude incidence rates increasing drastically from 62.8 at ages 35-39 to 271.4 at ages 50-54 (cases per 100,000 women). Various researchers have proposed methods and tools based on Machine Learning and Convolutional Neural Networks for assessing mammographic images, but these methods have produced detection and interpretation errors resulting in false-positive and false-negative cases when used in the real world. We believe that this problem can potentially be resolved by implementing effective image pre-processing techniques to create training data for Deep-CNN. Therefore, the main aim of this research is to propose effective image preprocessing methods to create datasets that can save computational time for the neural network and improve accuracy and classification rates. To do so, this research proposes methods for background removal, pectoral muscle removal, adding noise to the images, and image enhancements. Adding noise without affecting the quality of details in the images makes the input images for the neural network more representative, which may improve the performance of the neural network model when used in the real world. The proposed method for background removal is the "Rolling Ball Algorithm" and "Huang's Fuzzy Thresholding", which succeed in removing background from 100% of the images. For pectoral muscle removal "Canny Edge Detection" and "Hough's Line Transform" are used, which removed muscle from 99.06% of the images. "Invert", "CTI_RAS" and "ISOCONTOUR" lookup tables (LUTs) were used for image enhancements to outline the ROIs and regions within the ROIs.

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Section: A Background Removalmentioning

confidence: 99%

Section: B Cyclomatic Complexitymentioning

confidence: 99%

Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN

et al. 2021

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“…They detect and determine the defective areas using a black and white CCD camera. Then, a morphological segmentation [16,17] process is applied on the collected images to extract the texture orientation features of the leather. A few of qualitative results are shown in the paper, however, the quantitative methods and numerical data to evaluate the proposed algorithm are absence.…”

Section: Introductionmentioning

confidence: 99%

Automatic Defect Segmentation on Leather with Deep Learning

Liong¹,

Gan²,

Huang³

et al. 2019

Preprint

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Leather is a natural and durable material created through a process of tanning of hides and skins of animals. The price of the leather is subjective as it is highly sensitive to its quality and surface defects condition. In the literature, there are very few works investigating on the defects detection for leather using automatic image processing techniques. The manual defect inspection process is essential in an leather production industry to control the quality of the finished products. However, it is tedious, as it is labour intensive, time consuming, causes eye fatigue and often prone to human error. In this paper, a fully automatic defect detection and marking system on a calf leather is proposed. The proposed system consists of a piece of leather, LED light, high resolution camera and a robot arm. Succinctly, a machine vision method is presented to identify the position of the defects on the leather using a deep learning architecture. Then, a series of processes are conducted to predict the defect instances, including elicitation of the leather images with a robot arm, train and test the images using a deep learning architecture and determination of the boundary of the defects using mathematical derivation of the geometry. Note that, all the processes do not involve human intervention, except for the defect ground truths construction stage. The proposed algorithm is capable to exhibit 91.5% segmentation accuracy on the train data and 70.35% on the test data. We also report confusion matrix, F1-score, precision and specificity, sensitivity performance metrics to further verify the effectiveness of the proposed approach.

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“…MM can work well for segmenting the images that are heavily corrupted with noise. It has the capability to separate and eliminate the foreground noise, and the noisy background by applying several MM operators [65].…”

Section: Mathematical Morphologymentioning

confidence: 99%

Image segmentation for biometric identification systems

Said¹

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A mathematical morphological approach for segmenting heavily noise-corrupted images

Cited by 22 publications

References 12 publications

Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN

Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN

Automatic Defect Segmentation on Leather with Deep Learning

Image segmentation for biometric identification systems

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