An Improvement of Morphological Operation and Active Region Based Segmentation in Image Enhancement and Segmentation for Aspergillus Species

Radzuan, Nur Rodiatul Raudah Mohamed; Jaafar, Harlina Suzana; Zabani, Farah Nabilah; Nasir, Aimi Salihah Abdul

doi:10.1109/icsengt.2019.8906363

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…After segmentation process was completely done, the image obtained that displayed as binary image been fused with the original image (RGB image). This fusion was made to get the final result as coloured image, in line with the main aim of this section; segmentation of colour image [27,28]. Plus, imposed binary image on RGB image also help to make sure the particular area was cropping out nicely while preserved the features of Aspergillus sp.…”

Section: Fusionmentioning

confidence: 91%

Curvature-Based Active Region Segmentation for Improved Image Processing of Aspergillus Species

Nur Rodiatul Raudah Mohamed Radzuan,

Haryati Jaafar,

Farah Nabilah Zabani

et al. 2024

ARASET

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Aspergillus is one of the most ubiquitous of the airborne saprophytic fungi that can withstand various climatic conditions and could cause multiple type of illness. It can be beneficial to humankind and also can be infectious to humans and animals. Direct microscopic is used by trained microscopist as one of the alternatives in identification process to any specimen that suspected of having fungal infection. Confirmation towards identification is often necessary as the structure of Aspergillus is complex and dissimilar in each cycle. In addition, the structure of some species of Aspergillus are the almost same, which can be incorrectly recognized. In prevention of misidentification, computer-based Aspergillus species identification is proposed. The detection process is the earliest and important process hence, this paper proposed an active region-based segmentation method in order to detect the presence of fungi. This method is literally not depending on the gradient or sharp edges of the object and implementing level set function for curve evolution which able to reduce the computational cost. Originally, this function was developed for tracking fluid interfaces but in this study, this function has been applied to fungi database. Two different methods were tested and compared to observe their ability to segment different 80 of Aspergillus images which included four species. Experiments conducted have been compared with the baseline technique and the proposed method is outperformed in terms of accuracy, specificity with average of 90% and PSNR value of greater than 40dB. Meanwhile the active contour (snake) was slightly underperformed but well performed particularly in terms of sensitivity with greater than 80% for all the species. Moreover, upon scrutinizing the dice coefficients provided in both tables, it becomes apparent that there is a lack of significant variance in the values, except in the instance of Aspergillus fumigatus (active region-based) that which produces a result below 36%.

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Section: Fusionmentioning

confidence: 91%

Curvature-Based Active Region Segmentation for Improved Image Processing of Aspergillus Species

Nur Rodiatul Raudah Mohamed Radzuan,

Haryati Jaafar,

Farah Nabilah Zabani

et al. 2024

ARASET

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“…( 6). d y, x j = (y − x j ) T y − x j (6) where d y, x j is the Euclidean distance, x j is the training sample and y is the query point.…”

Section: Nearest Neighbor Classifiermentioning

confidence: 99%

“…It is still rendering some flaws related to diagnosis accuracy, fungal sensitization as well as time consumption. Moreover, most of Aspergillus species have overlapping morphological characteristics, which has allowed several genetically distinct species to be misidentified [5,6]. Even the process is handled by experienced microscopist, confirmation is often necessary.…”

Section: Introductionmentioning

confidence: 99%

An Identification of Aspergillus Species: A Comparison on Supervised Classification Methods

Radzuan

Jaafar

Nasir

2021

Lecture Notes in Electrical Engineering

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Aspergillus is one of the well-known existed saprophytic fungi that can withstand with various environments. Other can be beneficial in food industry, it also can be infectious to human and animals and normally, it attacks those with low immunity level. In order to keep the treatment in track with more accurate analysis, identification of Aspergillus plays an important role. Identification of Aspergillus is solely based on its characteristic and currently, there are two methods used which are microscopic and macroscopic examinations to observe its features. It handled by experienced microscopist and a few confirmations had to be done before presenting out the final result. Therefore, to prevent misidentification, an automated based identification is proposed. In this paper, different supervised classifiers are tested and compared to observe their ability to detect different 162 of Aspergillus images. The features have been extracted by using Principal component analysis (PCA) and several classifiers such as k-nearest neighbour (kNN), Sparse Representation Classifier (SRC), Support Vector Machine (SVM), Improved Fuzzy-Based k Nearest Centroid Neighbor (IFkNCN) and Kernal Sparse Representation Classifier (KSRC) are employed. Based on its accuracy, Aspergillus flavus recorded 80% of accuracy for all the classifiers.

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Breast Cancer Detection On X-Tray Mammogram Images

Azmi,

Mustafa,

Alquran

et al. 2023

2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC)

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An Improvement of Morphological Operation and Active Region Based Segmentation in Image Enhancement and Segmentation for Aspergillus Species

Cited by 4 publications

References 4 publications

Curvature-Based Active Region Segmentation for Improved Image Processing of Aspergillus Species

Curvature-Based Active Region Segmentation for Improved Image Processing of Aspergillus Species

An Identification of Aspergillus Species: A Comparison on Supervised Classification Methods

Breast Cancer Detection On X-Tray Mammogram Images

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