Next Article in Journal
Liquid-Crystal-on-Silicon for Augmented Reality Displays
Next Article in Special Issue
Non-Contact Ultrasonic Inspection of Impact Damage in Composite Laminates by Visualization of Lamb wave Propagation
Previous Article in Journal
Influence of Surface State in Micro-Welding of Copper by Nd:YAG Laser
Previous Article in Special Issue
Simulation of Real Defect Geometry and Its Detection Using Passive Magnetic Inspection (PMI) Method
Open AccessArticle
Peer-Review Record

Defect Detection of Aluminum Alloy Wheels in Radiography Images Using Adaptive Threshold and Morphological Reconstruction

Appl. Sci. 2018, 8(12), 2365;
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Appl. Sci. 2018, 8(12), 2365;
Received: 5 November 2018 / Revised: 20 November 2018 / Accepted: 20 November 2018 / Published: 23 November 2018
(This article belongs to the Special Issue Nondestructive Testing in Composite Materials)

Round 1

Reviewer 1 Report

Overall this is a good paper with sound theoretical base backed up with experimental verification. 

It would be useful if the authors could emphasize the novelty of the paper

1 the novelty in terms of Adaptive Threshold

2 the novelty in terms of Morphological Reconstruction

3 the novelty in terms of combining these together.

Author Response


We followed the suggestions and added a paragraph in section 4 to illustrate the novelty in terms of Adaptive Threshold,Morphological Reconstruction and their combination.Special thanks to you for your good comments.

The additions are as follows:

The innovation of the algorithm for dynamic threshold segmentation is that in case of defect segmentation, the algorithm focuses on the grayscale variation of the local area, and the size of local area and the grayscale variation can be directly determined by setting the parameters r and T, which is very suitable for the extraction of the hub defect. The morphological reconstruction operation restores the mask image by specifying the marking image. The operation feature is that the area existing in the marking image can be completely restored in the mask image, and the area not in the marking image will completely disappear in the mask image. Combining the algorithm for dynamic threshold segmentation with the morphological reconstruction operation is the max novelty in this paper. Taking the high threshold segmentation result in the dynamic threshold segmentation algorithm as the marking image, and the low threshold segmentation result as the mask image, the defect area marked by the high threshold segmentation result after the reconstruction operation can be completely restored, and the interference area generated by the low threshold segmentation result can be completely removed, and the accurate extraction of the hub defect is realized.


Reviewer 2 Report

Reviewer’s comments on the manuscript titled “Defect Detection of Aluminum Alloy Wheels in Radiography Images Using Adaptive Threshold and Morphological Reconstruction” by Junsheng Zhang, Zhijie Guo, Tengyun Jiao and Mingquan Wang for potential publication in Applied Sciences.


1.   The literature survey in the Introduction section is largely written in the present tense. It should be written in the past tense.

2.   While the results and discussion are presented in detail, the mathematical formulation is lacking. Section 2.1 Adaptive Threshold Segmentation should include more information and detailed formulation of the method and experimentation.


Given the above corrections are made and the manuscript is modified, the paper is recommended for potential publication in Applied Sciences.

Author Response

Response to 1:We are very sorry for our negligence of tense.We have made correction according to the Reviewer’s comments in red in the introduction.

Response to 2:It is really true as Reviewer suggested that the mathematical formulation of method is lacking. We have added two formulas and several paragraphs and 4 sets of specific image to illustrate the detailed information of the algorithm in Section 2.1.

We appreciate for Reviewers’ warm work earnestly, and hope that the correction will meet with approval.Once again, thank you very much for your comments and suggestions.

Some of  the additons are as follows:

The gray value g on each pixel of the smoothed image g(x, y) is jointly determined by the gray level of the corresponding pixels of the original image f(x, y) and the peripheral pixels. Assuming that the smoothing operator is h (x, y), then g (x, y) is obtained from the following formula:


 The symbol "*" indicates a convolution operation in the digital signal processing. The smoothing operator h(x, y) appears in the form of a matrix, usually being the mean smoothing operators and Gaussian smoothing operators. Taking the mean smoothing operator as an example, the h(x, y) expression when the size r is 3 shall be

Using the operator in formula (4), the original image f(x, y) is operated according to formula (3), and the gray value g of each point in the image g(x, y) that has been obtained is the average gray scale of a total of 9 pixels in a 3 × 3 square area that takes that point as the center. Fig. 1(a) shows a part of the gray value of the X-ray image of the hub, and the image g(x, y) obtained through 3 × 3 mean smoothing is shown in Fig. 1(b). Assuming that the fixed threshold T is set to 5, the actual threshold value of each point in Fig. 1(a) when it is processed by binarization based on formula (2) is the gray value corresponding to that point in Fig. 1(b) plus 5, that is, Fig. 1 ( c). Finally, the binarization result of Fig. 1(a) is equivalent to selecting a target object whose gray scale is larger than the average background by 5 in 3 × 3 local area, as shown in Fig. 1(d).




Author Response File: Author Response.docx

Back to TopTop