Analysis the digital images by using morphology operators

Saeed, Hadeel Amjed; Hamad, Sumaya; Hussain, Azmi Tawfik

doi:10.11591/ijeecs.v24.i3.pp1654-1662

Cited by 4 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar CNN solutions [12]- [18] differ from the one proposed in this paper given that the developed architecture, combined with the technologies presented in the following section, had better results and  ISSN: 2252-8938 Int J Artif Intell, Vol. 12, No.…”

mentioning

confidence: 68%

Deep learning based slope erosion detection

Limão

Araújo

Francês

2023

IJ-AI

View full text Add to dashboard Cite

Being increasingly present at the most diverse structure health monitoring (SHM) scenarios, many high-performance artificial intelligence techniques have been able to solve structural analysis problems. When it comes to image classification solutions, convolutional neural networks (CNNs) deliver the best results. This scenario encourages us to explore machine learning techniques, such as computer vision, and merge it with different technologies to achieve the best performance. This paper proposes a custom CNN architecture trained with slope erosion images that showed satisfactory results with an accuracy of 96.67%, enabling a precise and improved identification of instability indicators. These instabilities, when detected in advance, prevent disasters and enable proper maintenance to be carried out, given that its integrity directly affects structures built around and above it.

show abstract

mentioning

confidence: 68%

Deep learning based slope erosion detection

Limão

Araújo

Francês

2023

IJ-AI

View full text Add to dashboard Cite

show abstract

“…A shape known as a structuring element organizes the specified method and range of this thickening. While the purpose of erosion operation is to reduce the object size that exists in an image [12]. The definitions 2) respectively and as shown in Figure 1, where Figure 1(a) defines erosion and Figure 1(b) demonstrates dilation.…”

Section: Methods Background 21 Morphology Operationsmentioning

confidence: 99%

“…Segmentation, in addition to automated counting and testing, are the main areas of application. Morphology is a certain and crucial set of methods that can be treated mathematically precisely in the context of set theory [12]. So, by employing these morphology characteristics with the abilities of the proposed template of features and using appropriate structure element size after enhancing image quality and boosting the segmentation by edge operator an attempt to detect real pixel objects of the image is proposed and proved, and evaluated in the next sections.…”

Section: Introductionmentioning

confidence: 99%

Texture and pixel intensity characterization-based image segmentation with morphology and watershed techniques

Ibrahim

Al-Saleh

Jabar

2023

IJEECS

View full text Add to dashboard Cite

<p>Image segmentation is an image processing technique that concentrated on finding and locating the parts of an image such as objects and boundaries. The purpose of locating these parts is for use in further processing analysis of an image such as recognition tasks, and content-based image retrieval. This paper introduces the segmentation procedure using a proposed template of features with watershed or morphology operations. Features template based on segmentation process conveys pixels’ intensities property perceived by the threshold value of histogram representation and texture feature where the regions are characterized by their texture content using standard deviation (SD) filtering. Wiener filter and histogram equalization (HE) techniques are used as preprocessing operations to enhance the image quality. The edge detector operator is hybridized to boost the segmentation process. Some statistical metrics are used for assessing and analyzing the performance of the stages in the proposed work. As a result, this proposed template of features achieved more performance with watershed and morphology segmentation.</p>

show abstract

“…After getting the image results from the image segmentation and extraction process, the next step is to perform morphological operations based on the morphological features of the image [42], [43]. Morphology is an image processing process that focuses on the shape of objects [44]. Filling region is one of the morphological algorithms in image processing with the aim of selecting an area with a certain color [45].…”

Section: Morphological Operationsmentioning

confidence: 99%

Automated model for identification on mastoid of temporal bone image

Arlis

Defit

Sumijan

2023

IJEECS

View full text Add to dashboard Cite

Mastoiditis occurs due to inflammation that can affect the structure of the mastoid bone. The mastoid bone consists of the mastoid air cell system (MACS) which protects the ear structures and regulates air pressure in the ear and has different sizes and characteristics, making it very difficult to identify precisely. This study aims to identify and find the right MACS size by developing an automatic identification model and obtaining the optimal threshold value in the segmentation process using the extended adaptive threshold (eAT) method. The research dataset uses computed tomography (CT)-scan images of 308 slices of 12 patients indicated for mastoiditis. The results of this study provide identification that has the right MACS accuracy and size. Overall, the optimal segmentation process obtained the smallest threshold value of 57 and the largest threshold value of 63, the smallest MACS size is 4.025 cm2 and the largest is 8.816 cm2 with an accuracy rate of 93.4%. The smaller MACS size indicates inflammation in the mastoid area and these patients require more intensive treatment.

show abstract

Analysis the digital images by using morphology operators

Cited by 4 publications

References 17 publications

Deep learning based slope erosion detection

Deep learning based slope erosion detection

Texture and pixel intensity characterization-based image segmentation with morphology and watershed techniques

Automated model for identification on mastoid of temporal bone image

Contact Info

Product

Resources

About