The proposed technique was applied to two BUS datasets. The combined data set has 943 BUS images, 597 BUS with benign lesions, 263 with malignant lesions, and 133 non-lesion images. After closely analyzing the BUS, masked images were generated by the experts. Both datasets were analysed by the experts. Complete details about the datasets are available in , .
To represent the efficiency of the proposed technique, we have conducted a comparative analysis with other prevailing image enhancement techniques. There are two categories of quality metrics based on the involvement of reference images, namely Full Reference and No-Reference quality metrics. A Full reference quality metric determines the quality of the image after comparison with a reference image that is supposed to be of high quality. While the no-reference quality metric does not need a reference image, it takes the image and applies an algorithm to predict its quality. This quality assessment technique is also known as Objective blind image quality assessment. From previous segmentation results, it is clear that no high-quality reference image exists. In the absence of a high-quality reference image, the non-reference quality assessment metric was used.
The Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) metric defined in  is statistically better than many Full reference quality metrics. Another quality metric is Natural Image Quality Evaluator (NIQE) , which works on the natural scene static model and some static features to measure the deviation in statistical regularities from the natural image. Without any human evaluated distortion image, it is entirely blind. Along with NIQE and BRISQUE, we have also adopted the Entropy of the enhanced images.
Fig 3 has the pictorial presentation of the enhanced images, and Table 1 presents the average values of the quality metrics. All the metrics attend a lower value for a less distorted image and vice versa. BRISQUE, NIQE, and Entropy values for the original image (43.01, 7.744, and 7.083, respectively) set the standard for other enhancement images. Any improvement will be reflected by the lower metric values in the table. AGCWD has amplified the distortions already present in image 3(b). AGCWD has metrics values in Table 1 similar to the original image. The BRISQUE value for AGCWD is 43.45 slightly more significant than the original image. The CLAHE enhanced images show a minimal improvement in the metric values compared to the original image. In contrast, the techniques introduced by Prerna et al. and Roy et al. show far better performance than the CLAHE and AGCWD in Table 1. The reduction in distortion can be easily observed at the bottom of Fig 3 (j) and (k). The values achieved by the proposed technique for BRISQUE, NIQE, and Entropy are 38.87, 5.57, and 4.01, respectively. The proposed method has the least values for all metrics. It shows that the images enhanced by the proposed technique have the least distortion.
Here we have analyzed the result of various enhancement techniques segmented using AC and the MSS. After segmentation of the BUS image, we have generated the mask for corresponding segmentation for comparison. The mask images generated by the expert were used to find the similarity metric value. For quantitative evaluation, we used the DICE similarity metric, Jaccard similarity , and BF score . This study took the recent enhancement techniques to compare efficacy in lesion segmentation. Fig 4 shows the masked image of different techniques segmented using the AC method. Fig 4 (a) & (b) are the original BUS image with lesions of different sizes and shapes. The images enhanced using CWS have very much segmented the lesion boundary where the intensity change is significant. But for the boundary regions where the intensity variation is minimal, it fails to adhere to the lesion boundary. The average similarity metric values encountered across the datasets have been provided in Table 2, and CWS has the most negligible value of similarity metrics. For SHEHM, the similarity metric values in Table 2 are better than CWS but still not adequate. The masked version of the image enhanced using the SHEHM in Fig 4 (c) & (g) shows the penetration of the segmented regions in the surrounding. The masked image of the proposed technique completely outruns both methods for boundary detection in Fig 4 (d) & (h). The enhanced image’s segmentation adheres closest to the true lesion boundary. The values of Dice, Jaccard, and BF score for the mask of the image enhanced using the proposed technique are 0.8179, 0.69, and 0.4818, respectively.
Fig 5 shows the edge detection using the MSS and simple superpixel applied to the output image of the proposed technique. They have lesser spikes at the boundary than the masked image generated after AC. All of the three techniques for segmentation adhere well over the lesion boundary of the image enhanced using the proposed technique. Edges generated by superpixel in Fig 5 (c) and (f) have better boundaries than the edges of active contour-based segmentation in Fig 4 (d) and (h). Moreover, the similarity metric values for the superpixel-based edge detection are far better than others.
False Positive assessment
For a reliable CAD system, it’s crucial to analyse the system slump towards false positives. To verify this, we applied the proposed technique to the non-lesion images. The segmentation of the enhanced image must not adhere to some random intensity anomaly. We have calculated the probability of false-positive across the dataset as an evaluation metric in Table 3. Fig 6 (a) and (b) are two original non-lesion images from the dataset. The enhanced images were segmented using the AC method. The lesion candidates were selected based on standard features used in different studies , . The CLAHE and the AGCWD enhanced images have caught up to some intensity anomalies in Fig 3 (b, c, h & j). And from the table, it is clear that both techniques are too prone to false positives. The performance of the de-noising technique is better than the CLAHE and AGCWD. But its segmentation using AC also caught up to a slight variation in BUS. The image enhancement technique proposed by Roy et al. was just better than CWS. However, the performance of all the techniques was competitive which each other but still not adequate for common practice. Fig 6 (l) & (f) the masked image has no traces of any blob-like structure. It means no trace of a lesion in that BUS, and it also proves that the segmentation of the proposed technique is not vulnerable to random grayscale anomalies.