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. 2020 Jun 1;10(3):261-272.
doi: 10.31661/jbpe.v0i0.1197. eCollection 2020 Jun.

Optimizing Infrared Camera Resolution for Small Object Detection using Subpixel Rendering and PIFS in Multiresolution Image Analysis

Poerbaningtyas E  1   2 , Dradjat R S  3 , Endharti A T  4 , Sakti S P  5 , Widjajanto E  6 , Yueniwati Y  7 , Purnomo M H  8
Affiliations

Optimizing Infrared Camera Resolution for Small Object Detection using Subpixel Rendering and PIFS in Multiresolution Image Analysis

Poerbaningtyas E et al. J Biomed Phys Eng. .

Abstract

Background: Breast cancer screening techniques have been developing rapidly in the field of imaging systems. One of these techniques is thermography, which is an alternative modality for mammography to detect breast lesions. Thermography utilization has been progressively developing as various models and methods of object processing improvement. Currently, the Fluke TIS20 infrared camera, with a resolution of 320 × 240, has not been used to measure precisely small objects such as early breast cancer lesions. Retrieval and processing of single images lead into imprecise object measurements and false positive results.

Objective: Problems have been arisen due to the limitations of the camera resolution, object retrieval techniques and suboptimal image processing. The aim of this study was to detect accurately breast cancer lesions in rats, which were induced by carcinogenic compounds.

Material and methods: In this experimental study, development of models was conducted based on increasing image by optimizing the ability of low-resolution infrared (IR) cameras to identify s mall objects precisely. Image pixel density increased by adjusting the distance of the objects from the camera and multiple images of objects gradually shifting were used to measure object dimensions precisely.

Results: The results showed that cancerous lesions as small as 1.27 mm could be detected. This method of lesion detection had a sensitivity and specificity of 93% and 77 % respectively.

Conclusion: Small objects (cancerous lesions) were measured by increasing image resolution through splitting pixels into subpixels and combining several images using Partitioned Iterated Function Systems (PIFS).

Keywords: Breast Neoplasms; Mammography; Measure; Screening Cancer; Temperature.

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Figures

Figure 1
Figure 1
a) The size of an object is smaller than a pixel box so that it cannot measure the size of an object precisely. b) The effect of setting the retrieval distance of an object will reduce pixel density so that the size of the object can be measured at least the size of a pixel box.
Figure 2
Figure 2
Mapping of large and small squares in PIFS [15]. The definition of PIFS is in the form of an image, where large squares are a combination of small squares overlapping with each other.
Figure 3
Figure 3
a) Thermography results b) contrast enhancement of imaging using PIFS [16]. Thermographic output in the form of temperature image and lesion reading were performed by increasing image contrast using PIFS.
Figure 4
Figure 4
a) Camera supporting to adjust the distance between the camera and the object causes the optimal pixel resolution of the camera resolution to be better. b) “XY linear “table is to shift object right, left, back and forth, and get many images.
Figure 5
Figure 5
a) Visual Rat: location of breast cancer in research objects. b) Thermal imaging of rat heat information on an object in the form of images, where white shows the highest temperature.
Figure 6
Figure 6
the image describes the creation of a subpixel, which breaks 1 pixel into 4 pixels. The hot temperature of the pixel will be copied to 4 pixel fractions, so that the original image resolution changes from 320 × 240 to 640 × 480.
Figure 7
Figure 7
Subpixel creation. The image describes the value of each pixel in subpixel with the same value.
Figure 8
Figure 8
The position of an object at a pixel intersection and data collection is repeated up to 25 times where the shift per image is 0.23 mm using the “XY Linear” table.
Figure 9
Figure 9
Combining 25 images into 1 image shows the determination of precision by applying the PIFS method merging 25 images (multi images) into 1 image, with an average value of hot temperature degrees in each pixel per image.
Figure 10
Figure 10
Combining 25 images into 1 image using PIFS modeling.
Figure 11
Figure 11
Graph of temperature relations with the size of cancer lesions. The figure shows a strong correlation between high temperatures proportional to the size of cancer lesions.
Figure 12
Figure 12
a) Temperature distribution in first model. b) Temperature distribution in the second model.
See this image and copyright information in PMC

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