In order to improve the treatment accuracy of patients with different subtypes of bladder prolapse (BP), the value of ultrasonic images based on fuzzy theory (FT) and wavelet transform (WT) algorithm for detection and classification of subtypes of BP was discussed. First, the effects
of fuzzy enhancement (FE) algorithm, WT enhancement algorithm, and FT combined wavelet algorithm on medical ultrasonic images were compared. Then, 144 cases of BP patients admitted to our hospital from October 2017 to October 2019 were selected as study objects. Ultrasound technology was used
to examine the patient's bottom information. Finally, the data of posterior urethra-vesical angle (PUVA), rotation angle (RA) of urethra, mobility of bladder neck, lowest point of bladder and lowest point of posterior wall of bladder were measured under resting and Vslsalva conditions. According
to Green classification, different subtypes of bladder prolapse were distinguished by the measurement data of pelvic floor (PF) ultrasound image. The clinical characteristics of Stress Urinary Incontinence (SUI), dysuria, and frequent urination of subtypes of BP were compared. The results
showed that the ultrasonic image quality was the best by combining FT with WT; BP type II PUVA was more than 140°, BP type III PUVA was less than 140°, and the difference between the two was statistically significant (P < 0.05); the lowest point of the bladder in patients
with type III BP was significantly higher than that in patients with type II BP (P < 0.05), and the neck mobility of type III bladder and the lowest point of the posterior wall of the bladder were significantly lower than that in patients with type II BP (P < 0.05); the
difference of urethral RA between type II and type III of BP was not statistically significant (P > 0.05); the incidence of SUI in patients with type II BP was higher than that of type III BP (P < 0.05), and the incidence of dysuria in patients with type III BP was higher
than that of type II BP (P < 0.05); the incidence of urinary frequency in patients with type II and type III BP was not statistically significant (P > 0.05), which showed that PF ultrasound based on FT and WT algorithm could effectively detect and identify different subtypes
of BP.
